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REALIZING THE PROMISE:

Leading up to the 75th anniversary of the UN General Assembly, this “Realizing the promise: How can education technology improve learning for all?” publication kicks off the Center for Universal Education’s first playbook in a series to help improve education around the world.

It is intended as an evidence-based tool for ministries of education, particularly in low- and middle-income countries, to adopt and more successfully invest in education technology.

While there is no single education initiative that will achieve the same results everywhere—as school systems differ in learners and educators, as well as in the availability and quality of materials and technologies—an important first step is understanding how technology is used given specific local contexts and needs.

The surveys in this playbook are designed to be adapted to collect this information from educators, learners, and school leaders and guide decisionmakers in expanding the use of technology.  

Introduction

While technology has disrupted most sectors of the economy and changed how we communicate, access information, work, and even play, its impact on schools, teaching, and learning has been much more limited. We believe that this limited impact is primarily due to technology being been used to replace analog tools, without much consideration given to playing to technology’s comparative advantages. These comparative advantages, relative to traditional “chalk-and-talk” classroom instruction, include helping to scale up standardized instruction, facilitate differentiated instruction, expand opportunities for practice, and increase student engagement. When schools use technology to enhance the work of educators and to improve the quality and quantity of educational content, learners will thrive.

Further, COVID-19 has laid bare that, in today’s environment where pandemics and the effects of climate change are likely to occur, schools cannot always provide in-person education—making the case for investing in education technology.

Here we argue for a simple yet surprisingly rare approach to education technology that seeks to:

• Understand the needs, infrastructure, and capacity of a school system—the diagnosis;
• Survey the best available evidence on interventions that match those conditions—the evidence; and
• Closely monitor the results of innovations before they are scaled up—the prognosis.

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The framework.

Our approach builds on a simple yet intuitive theoretical framework created two decades ago by two of the most prominent education researchers in the United States, David K. Cohen and Deborah Loewenberg Ball. They argue that what matters most to improve learning is the interactions among educators and learners around educational materials. We believe that the failed school-improvement efforts in the U.S. that motivated Cohen and Ball’s framework resemble the ed-tech reforms in much of the developing world to date in the lack of clarity improving the interactions between educators, learners, and the educational material. We build on their framework by adding parents as key agents that mediate the relationships between learners and educators and the material (Figure 1).

Figure 1: The instructional core

Adapted from Cohen and Ball (1999)

As the figure above suggests, ed-tech interventions can affect the instructional core in a myriad of ways. Yet, just because technology can do something, it does not mean it should. School systems in developing countries differ along many dimensions and each system is likely to have different needs for ed-tech interventions, as well as different infrastructure and capacity to enact such interventions.

The diagnosis:

How can school systems assess their needs and preparedness.

A useful first step for any school system to determine whether it should invest in education technology is to diagnose its:

• Specific needs to improve student learning (e.g., raising the average level of achievement, remediating gaps among low performers, and challenging high performers to develop higher-order skills);
• Infrastructure to adopt technology-enabled solutions (e.g., electricity connection, availability of space and outlets, stock of computers, and Internet connectivity at school and at learners’ homes); and
• Capacity to integrate technology in the instructional process (e.g., learners’ and educators’ level of familiarity and comfort with hardware and software, their beliefs about the level of usefulness of technology for learning purposes, and their current uses of such technology).

Before engaging in any new data collection exercise, school systems should take full advantage of existing administrative data that could shed light on these three main questions. This could be in the form of internal evaluations but also international learner assessments, such as the Program for International Student Assessment (PISA), the Trends in International Mathematics and Science Study (TIMSS), and/or the Progress in International Literacy Study (PIRLS), and the Teaching and Learning International Study (TALIS). But if school systems lack information on their preparedness for ed-tech reforms or if they seek to complement existing data with a richer set of indicators, we developed a set of surveys for learners, educators, and school leaders. Download the full report to see how we map out the main aspects covered by these surveys, in hopes of highlighting how they could be used to inform decisions around the adoption of ed-tech interventions.

The evidence:

How can school systems identify promising ed-tech interventions.

There is no single “ed-tech” initiative that will achieve the same results everywhere, simply because school systems differ in learners and educators, as well as in the availability and quality of materials and technologies. Instead, to realize the potential of education technology to accelerate student learning, decisionmakers should focus on four potential uses of technology that play to its comparative advantages and complement the work of educators to accelerate student learning (Figure 2). These comparative advantages include:

• Scaling up quality instruction, such as through prerecorded quality lessons.
• Facilitating differentiated instruction, through, for example, computer-adaptive learning and live one-on-one tutoring.
• Expanding opportunities to practice.
• Increasing learner engagement through videos and games.

Figure 2: Comparative advantages of technology

Here we review the evidence on ed-tech interventions from 37 studies in 20 countries*, organizing them by comparative advantage. It’s important to note that ours is not the only way to classify these interventions (e.g., video tutorials could be considered as a strategy to scale up instruction or increase learner engagement), but we believe it may be useful to highlight the needs that they could address and why technology is well positioned to do so.

When discussing specific studies, we report the magnitude of the effects of interventions using standard deviations (SDs). SDs are a widely used metric in research to express the effect of a program or policy with respect to a business-as-usual condition (e.g., test scores). There are several ways to make sense of them. One is to categorize the magnitude of the effects based on the results of impact evaluations. In developing countries, effects below 0.1 SDs are considered to be small, effects between 0.1 and 0.2 SDs are medium, and those above 0.2 SDs are large (for reviews that estimate the average effect of groups of interventions, called “meta analyses,” see e.g., Conn, 2017; Kremer, Brannen, & Glennerster, 2013; McEwan, 2014; Snilstveit et al., 2015; Evans & Yuan, 2020.)

*In surveying the evidence, we began by compiling studies from prior general and ed-tech specific evidence reviews that some of us have written and from ed-tech reviews conducted by others. Then, we tracked the studies cited by the ones we had previously read and reviewed those, as well. In identifying studies for inclusion, we focused on experimental and quasi-experimental evaluations of education technology interventions from pre-school to secondary school in low- and middle-income countries that were released between 2000 and 2020. We only included interventions that sought to improve student learning directly (i.e., students’ interaction with the material), as opposed to interventions that have impacted achievement indirectly, by reducing teacher absence or increasing parental engagement. This process yielded 37 studies in 20 countries (see the full list of studies in Appendix B).

Scaling up standardized instruction

One of the ways in which technology may improve the quality of education is through its capacity to deliver standardized quality content at scale. This feature of technology may be particularly useful in three types of settings: (a) those in “hard-to-staff” schools (i.e., schools that struggle to recruit educators with the requisite training and experience—typically, in rural and/or remote areas) (see, e.g., Urquiola & Vegas, 2005); (b) those in which many educators are frequently absent from school (e.g., Chaudhury, Hammer, Kremer, Muralidharan, & Rogers, 2006; Muralidharan, Das, Holla, & Mohpal, 2017); and/or (c) those in which educators have low levels of pedagogical and subject matter expertise (e.g., Bietenbeck, Piopiunik, & Wiederhold, 2018; Bold et al., 2017; Metzler & Woessmann, 2012; Santibañez, 2006) and do not have opportunities to observe and receive feedback (e.g., Bruns, Costa, & Cunha, 2018; Cilliers, Fleisch, Prinsloo, & Taylor, 2018). Technology could address this problem by: (a) disseminating lessons delivered by qualified educators to a large number of learners (e.g., through prerecorded or live lessons); (b) enabling distance education (e.g., for learners in remote areas and/or during periods of school closures); and (c) distributing hardware preloaded with educational materials.

Prerecorded lessons

Technology seems to be well placed to amplify the impact of effective educators by disseminating their lessons. Evidence on the impact of prerecorded lessons is encouraging, but not conclusive. Some initiatives that have used short instructional videos to complement regular instruction, in conjunction with other learning materials, have raised student learning on independent assessments. For example, Beg et al. (2020) evaluated an initiative in Punjab, Pakistan in which grade 8 classrooms received an intervention that included short videos to substitute live instruction, quizzes for learners to practice the material from every lesson, tablets for educators to learn the material and follow the lesson, and LED screens to project the videos onto a classroom screen. After six months, the intervention improved the performance of learners on independent tests of math and science by 0.19 and 0.24 SDs, respectively but had no discernible effect on the math and science section of Punjab’s high-stakes exams.

One study suggests that approaches that are far less technologically sophisticated can also improve learning outcomes—especially, if the business-as-usual instruction is of low quality. For example, Naslund-Hadley, Parker, and Hernandez-Agramonte (2014) evaluated a preschool math program in Cordillera, Paraguay that used audio segments and written materials four days per week for an hour per day during the school day. After five months, the intervention improved math scores by 0.16 SDs, narrowing gaps between low- and high-achieving learners, and between those with and without educators with formal training in early childhood education.

Yet, the integration of prerecorded material into regular instruction has not always been successful. For example, de Barros (2020) evaluated an intervention that combined instructional videos for math and science with infrastructure upgrades (e.g., two “smart” classrooms, two TVs, and two tablets), printed workbooks for students, and in-service training for educators of learners in grades 9 and 10 in Haryana, India (all materials were mapped onto the official curriculum). After 11 months, the intervention negatively impacted math achievement (by 0.08 SDs) and had no effect on science (with respect to business as usual classes). It reduced the share of lesson time that educators devoted to instruction and negatively impacted an index of instructional quality. Likewise, Seo (2017) evaluated several combinations of infrastructure (solar lights and TVs) and prerecorded videos (in English and/or bilingual) for grade 11 students in northern Tanzania and found that none of the variants improved student learning, even when the videos were used. The study reports effects from the infrastructure component across variants, but as others have noted (Muralidharan, Romero, & Wüthrich, 2019), this approach to estimating impact is problematic.

A very similar intervention delivered after school hours, however, had sizeable effects on learners’ basic skills. Chiplunkar, Dhar, and Nagesh (2020) evaluated an initiative in Chennai (the capital city of the state of Tamil Nadu, India) delivered by the same organization as above that combined short videos that explained key concepts in math and science with worksheets, facilitator-led instruction, small groups for peer-to-peer learning, and occasional career counseling and guidance for grade 9 students. These lessons took place after school for one hour, five times a week. After 10 months, it had large effects on learners’ achievement as measured by tests of basic skills in math and reading, but no effect on a standardized high-stakes test in grade 10 or socio-emotional skills (e.g., teamwork, decisionmaking, and communication).

Drawing general lessons from this body of research is challenging for at least two reasons. First, all of the studies above have evaluated the impact of prerecorded lessons combined with several other components (e.g., hardware, print materials, or other activities). Therefore, it is possible that the effects found are due to these additional components, rather than to the recordings themselves, or to the interaction between the two (see Muralidharan, 2017 for a discussion of the challenges of interpreting “bundled” interventions). Second, while these studies evaluate some type of prerecorded lessons, none examines the content of such lessons. Thus, it seems entirely plausible that the direction and magnitude of the effects depends largely on the quality of the recordings (e.g., the expertise of the educator recording it, the amount of preparation that went into planning the recording, and its alignment with best teaching practices).

These studies also raise three important questions worth exploring in future research. One of them is why none of the interventions discussed above had effects on high-stakes exams, even if their materials are typically mapped onto the official curriculum. It is possible that the official curricula are simply too challenging for learners in these settings, who are several grade levels behind expectations and who often need to reinforce basic skills (see Pritchett & Beatty, 2015). Another question is whether these interventions have long-term effects on teaching practices. It seems plausible that, if these interventions are deployed in contexts with low teaching quality, educators may learn something from watching the videos or listening to the recordings with learners. Yet another question is whether these interventions make it easier for schools to deliver instruction to learners whose native language is other than the official medium of instruction.

Distance education

Technology can also allow learners living in remote areas to access education. The evidence on these initiatives is encouraging. For example, Johnston and Ksoll (2017) evaluated a program that broadcasted live instruction via satellite to rural primary school students in the Volta and Greater Accra regions of Ghana. For this purpose, the program also equipped classrooms with the technology needed to connect to a studio in Accra, including solar panels, a satellite modem, a projector, a webcam, microphones, and a computer with interactive software. After two years, the intervention improved the numeracy scores of students in grades 2 through 4, and some foundational literacy tasks, but it had no effect on attendance or classroom time devoted to instruction, as captured by school visits. The authors interpreted these results as suggesting that the gains in achievement may be due to improving the quality of instruction that children received (as opposed to increased instructional time). Naik, Chitre, Bhalla, and Rajan (2019) evaluated a similar program in the Indian state of Karnataka and also found positive effects on learning outcomes, but it is not clear whether those effects are due to the program or due to differences in the groups of students they compared to estimate the impact of the initiative.

In one context (Mexico), this type of distance education had positive long-term effects. Navarro-Sola (2019) took advantage of the staggered rollout of the telesecundarias (i.e., middle schools with lessons broadcasted through satellite TV) in 1968 to estimate its impact. The policy had short-term effects on students’ enrollment in school: For every telesecundaria per 50 children, 10 students enrolled in middle school and two pursued further education. It also had a long-term influence on the educational and employment trajectory of its graduates. Each additional year of education induced by the policy increased average income by nearly 18 percent. This effect was attributable to more graduates entering the labor force and shifting from agriculture and the informal sector. Similarly, Fabregas (2019) leveraged a later expansion of this policy in 1993 and found that each additional telesecundaria per 1,000 adolescents led to an average increase of 0.2 years of education, and a decline in fertility for women, but no conclusive evidence of long-term effects on labor market outcomes.

It is crucial to interpret these results keeping in mind the settings where the interventions were implemented. As we mention above, part of the reason why they have proven effective is that the “counterfactual” conditions for learning (i.e., what would have happened to learners in the absence of such programs) was either to not have access to schooling or to be exposed to low-quality instruction. School systems interested in taking up similar interventions should assess the extent to which their learners (or parts of their learner population) find themselves in similar conditions to the subjects of the studies above. This illustrates the importance of assessing the needs of a system before reviewing the evidence.

Preloaded hardware

Technology also seems well positioned to disseminate educational materials. Specifically, hardware (e.g., desktop computers, laptops, or tablets) could also help deliver educational software (e.g., word processing, reference texts, and/or games). In theory, these materials could not only undergo a quality assurance review (e.g., by curriculum specialists and educators), but also draw on the interactions with learners for adjustments (e.g., identifying areas needing reinforcement) and enable interactions between learners and educators.

In practice, however, most initiatives that have provided learners with free computers, laptops, and netbooks do not leverage any of the opportunities mentioned above. Instead, they install a standard set of educational materials and hope that learners find them helpful enough to take them up on their own. Students rarely do so, and instead use the laptops for recreational purposes—often, to the detriment of their learning (see, e.g., Malamud & Pop-Eleches, 2011). In fact, free netbook initiatives have not only consistently failed to improve academic achievement in math or language (e.g., Cristia et al., 2017), but they have had no impact on learners’ general computer skills (e.g., Beuermann et al., 2015). Some of these initiatives have had small impacts on cognitive skills, but the mechanisms through which those effects occurred remains unclear.

To our knowledge, the only successful deployment of a free laptop initiative was one in which a team of researchers equipped the computers with remedial software. Mo et al. (2013) evaluated a version of the One Laptop per Child (OLPC) program for grade 3 students in migrant schools in Beijing, China in which the laptops were loaded with a remedial software mapped onto the national curriculum for math (similar to the software products that we discuss under “practice exercises” below). After nine months, the program improved math achievement by 0.17 SDs and computer skills by 0.33 SDs. If a school system decides to invest in free laptops, this study suggests that the quality of the software on the laptops is crucial.

To date, however, the evidence suggests that children do not learn more from interacting with laptops than they do from textbooks. For example, Bando, Gallego, Gertler, and Romero (2016) compared the effect of free laptop and textbook provision in 271 elementary schools in disadvantaged areas of Honduras. After seven months, students in grades 3 and 6 who had received the laptops performed on par with those who had received the textbooks in math and language. Further, even if textbooks essentially become obsolete at the end of each school year, whereas laptops can be reloaded with new materials for each year, the costs of laptop provision (not just the hardware, but also the technical assistance, Internet, and training associated with it) are not yet low enough to make them a more cost-effective way of delivering content to learners.

Evidence on the provision of tablets equipped with software is encouraging but limited. For example, de Hoop et al. (2020) evaluated a composite intervention for first grade students in Zambia’s Eastern Province that combined infrastructure (electricity via solar power), hardware (projectors and tablets), and educational materials (lesson plans for educators and interactive lessons for learners, both loaded onto the tablets and mapped onto the official Zambian curriculum). After 14 months, the intervention had improved student early-grade reading by 0.4 SDs, oral vocabulary scores by 0.25 SDs, and early-grade math by 0.22 SDs. It also improved students’ achievement by 0.16 on a locally developed assessment. The multifaceted nature of the program, however, makes it challenging to identify the components that are driving the positive effects. Pitchford (2015) evaluated an intervention that provided tablets equipped with educational “apps,” to be used for 30 minutes per day for two months to develop early math skills among students in grades 1 through 3 in Lilongwe, Malawi. The evaluation found positive impacts in math achievement, but the main study limitation is that it was conducted in a single school.

Facilitating differentiated instruction

Another way in which technology may improve educational outcomes is by facilitating the delivery of differentiated or individualized instruction. Most developing countries massively expanded access to schooling in recent decades by building new schools and making education more affordable, both by defraying direct costs, as well as compensating for opportunity costs (Duflo, 2001; World Bank, 2018). These initiatives have not only rapidly increased the number of learners enrolled in school, but have also increased the variability in learner’ preparation for schooling. Consequently, a large number of learners perform well below grade-based curricular expectations (see, e.g., Duflo, Dupas, & Kremer, 2011; Pritchett & Beatty, 2015). These learners are unlikely to get much from “one-size-fits-all” instruction, in which a single educator delivers instruction deemed appropriate for the middle (or top) of the achievement distribution (Banerjee & Duflo, 2011). Technology could potentially help these learners by providing them with: (a) instruction and opportunities for practice that adjust to the level and pace of preparation of each individual (known as “computer-adaptive learning” (CAL)); or (b) live, one-on-one tutoring.

Computer-adaptive learning

One of the main comparative advantages of technology is its ability to diagnose students’ initial learning levels and assign students to instruction and exercises of appropriate difficulty. No individual educator—no matter how talented—can be expected to provide individualized instruction to all learners in his/her class simultaneously . In this respect, technology is uniquely positioned to complement traditional teaching. This use of technology could help learners master basic skills and help them get more out of schooling.

Although many software products evaluated in recent years have been categorized as CAL, many rely on a relatively coarse level of differentiation at an initial stage (e.g., a diagnostic test) without further differentiation. We discuss these initiatives under the category of “increasing opportunities for practice” below. CAL initiatives complement an initial diagnostic with dynamic adaptation (i.e., at each response or set of responses from learners) to adjust both the initial level of difficulty and rate at which it increases or decreases, depending on whether learners’ responses are correct or incorrect.

Existing evidence on this specific type of programs is highly promising. Most famously, Banerjee et al. (2007) evaluated CAL software in Vadodara, in the Indian state of Gujarat, in which grade 4 students were offered two hours of shared computer time per week before and after school, during which they played games that involved solving math problems. The level of difficulty of such problems adjusted based on students’ answers. This program improved math achievement by 0.35 and 0.47 SDs after one and two years of implementation, respectively. Consistent with the promise of personalized learning, the software improved achievement for all students. In fact, one year after the end of the program, students assigned to the program still performed 0.1 SDs better than those assigned to a business as usual condition. More recently, Muralidharan, et al. (2019) evaluated a “blended learning” initiative in which students in grades 4 through 9 in Delhi, India received 45 minutes of interaction with CAL software for math and language, and 45 minutes of small group instruction before or after going to school. After only 4.5 months, the program improved achievement by 0.37 SDs in math and 0.23 SDs in Hindi. While all learners benefited from the program in absolute terms, the lowest performing learners benefited the most in relative terms, since they were learning very little in school.

We see two important limitations from this body of research. First, to our knowledge, none of these initiatives has been evaluated when implemented during the school day. Therefore, it is not possible to distinguish the effect of the adaptive software from that of additional instructional time. Second, given that most of these programs were facilitated by local instructors, attempts to distinguish the effect of the software from that of the instructors has been mostly based on noncausal evidence. A frontier challenge in this body of research is to understand whether CAL software can increase the effectiveness of school-based instruction by substituting part of the regularly scheduled time for math and language instruction.

Live one-on-one tutoring

Recent improvements in the speed and quality of videoconferencing, as well as in the connectivity of remote areas, have enabled yet another way in which technology can help personalization: live (i.e., real-time) one-on-one tutoring. While the evidence on in-person tutoring is scarce in developing countries, existing studies suggest that this approach works best when it is used to personalize instruction (see, e.g., Banerjee et al., 2007; Banerji, Berry, & Shotland, 2015; Cabezas, Cuesta, & Gallego, 2011).

There are almost no studies on the impact of online tutoring—possibly, due to the lack of hardware and Internet connectivity in low- and middle-income countries. One exception is Chemin and Oledan (2020)’s recent evaluation of an online tutoring program for grade 6 students in Kianyaga, Kenya to learn English from volunteers from a Canadian university via Skype ( videoconferencing software) for one hour per week after school. After 10 months, program beneficiaries performed 0.22 SDs better in a test of oral comprehension, improved their comfort using technology for learning, and became more willing to engage in cross-cultural communication. Importantly, while the tutoring sessions used the official English textbooks and sought in part to help learners with their homework, tutors were trained on several strategies to teach to each learner’s individual level of preparation, focusing on basic skills if necessary. To our knowledge, similar initiatives within a country have not yet been rigorously evaluated.

Expanding opportunities for practice

A third way in which technology may improve the quality of education is by providing learners with additional opportunities for practice. In many developing countries, lesson time is primarily devoted to lectures, in which the educator explains the topic and the learners passively copy explanations from the blackboard. This setup leaves little time for in-class practice. Consequently, learners who did not understand the explanation of the material during lecture struggle when they have to solve homework assignments on their own. Technology could potentially address this problem by allowing learners to review topics at their own pace.

Practice exercises

Technology can help learners get more out of traditional instruction by providing them with opportunities to implement what they learn in class. This approach could, in theory, allow some learners to anchor their understanding of the material through trial and error (i.e., by realizing what they may not have understood correctly during lecture and by getting better acquainted with special cases not covered in-depth in class).

Existing evidence on practice exercises reflects both the promise and the limitations of this use of technology in developing countries. For example, Lai et al. (2013) evaluated a program in Shaanxi, China where students in grades 3 and 5 were required to attend two 40-minute remedial sessions per week in which they first watched videos that reviewed the material that had been introduced in their math lessons that week and then played games to practice the skills introduced in the video. After four months, the intervention improved math achievement by 0.12 SDs. Many other evaluations of comparable interventions have found similar small-to-moderate results (see, e.g., Lai, Luo, Zhang, Huang, & Rozelle, 2015; Lai et al., 2012; Mo et al., 2015; Pitchford, 2015). These effects, however, have been consistently smaller than those of initiatives that adjust the difficulty of the material based on students’ performance (e.g., Banerjee et al., 2007; Muralidharan, et al., 2019). We hypothesize that these programs do little for learners who perform several grade levels behind curricular expectations, and who would benefit more from a review of foundational concepts from earlier grades.

We see two important limitations from this research. First, most initiatives that have been evaluated thus far combine instructional videos with practice exercises, so it is hard to know whether their effects are driven by the former or the latter. In fact, the program in China described above allowed learners to ask their peers whenever they did not understand a difficult concept, so it potentially also captured the effect of peer-to-peer collaboration. To our knowledge, no studies have addressed this gap in the evidence.

Second, most of these programs are implemented before or after school, so we cannot distinguish the effect of additional instructional time from that of the actual opportunity for practice. The importance of this question was first highlighted by Linden (2008), who compared two delivery mechanisms for game-based remedial math software for students in grades 2 and 3 in a network of schools run by a nonprofit organization in Gujarat, India: one in which students interacted with the software during the school day and another one in which students interacted with the software before or after school (in both cases, for three hours per day). After a year, the first version of the program had negatively impacted students’ math achievement by 0.57 SDs and the second one had a null effect. This study suggested that computer-assisted learning is a poor substitute for regular instruction when it is of high quality, as was the case in this well-functioning private network of schools.

In recent years, several studies have sought to remedy this shortcoming. Mo et al. (2014) were among the first to evaluate practice exercises delivered during the school day. They evaluated an initiative in Shaanxi, China in which students in grades 3 and 5 were required to interact with the software similar to the one in Lai et al. (2013) for two 40-minute sessions per week. The main limitation of this study, however, is that the program was delivered during regularly scheduled computer lessons, so it could not determine the impact of substituting regular math instruction. Similarly, Mo et al. (2020) evaluated a self-paced and a teacher-directed version of a similar program for English for grade 5 students in Qinghai, China. Yet, the key shortcoming of this study is that the teacher-directed version added several components that may also influence achievement, such as increased opportunities for teachers to provide students with personalized assistance when they struggled with the material. Ma, Fairlie, Loyalka, and Rozelle (2020) compared the effectiveness of additional time-delivered remedial instruction for students in grades 4 to 6 in Shaanxi, China through either computer-assisted software or using workbooks. This study indicates whether additional instructional time is more effective when using technology, but it does not address the question of whether school systems may improve the productivity of instructional time during the school day by substituting educator-led with computer-assisted instruction.

Increasing learner engagement

Another way in which technology may improve education is by increasing learners’ engagement with the material. In many school systems, regular “chalk and talk” instruction prioritizes time for educators’ exposition over opportunities for learners to ask clarifying questions and/or contribute to class discussions. This, combined with the fact that many developing-country classrooms include a very large number of learners (see, e.g., Angrist & Lavy, 1999; Duflo, Dupas, & Kremer, 2015), may partially explain why the majority of those students are several grade levels behind curricular expectations (e.g., Muralidharan, et al., 2019; Muralidharan & Zieleniak, 2014; Pritchett & Beatty, 2015). Technology could potentially address these challenges by: (a) using video tutorials for self-paced learning and (b) presenting exercises as games and/or gamifying practice.

Video tutorials

Technology can potentially increase learner effort and understanding of the material by finding new and more engaging ways to deliver it. Video tutorials designed for self-paced learning—as opposed to videos for whole class instruction, which we discuss under the category of “prerecorded lessons” above—can increase learner effort in multiple ways, including: allowing learners to focus on topics with which they need more help, letting them correct errors and misconceptions on their own, and making the material appealing through visual aids. They can increase understanding by breaking the material into smaller units and tackling common misconceptions.

In spite of the popularity of instructional videos, there is relatively little evidence on their effectiveness. Yet, two recent evaluations of different versions of the Khan Academy portal, which mainly relies on instructional videos, offer some insight into their impact. First, Ferman, Finamor, and Lima (2019) evaluated an initiative in 157 public primary and middle schools in five cities in Brazil in which the teachers of students in grades 5 and 9 were taken to the computer lab to learn math from the platform for 50 minutes per week. The authors found that, while the intervention slightly improved learners’ attitudes toward math, these changes did not translate into better performance in this subject. The authors hypothesized that this could be due to the reduction of teacher-led math instruction.

More recently, Büchel, Jakob, Kühnhanss, Steffen, and Brunetti (2020) evaluated an after-school, offline delivery of the Khan Academy portal in grades 3 through 6 in 302 primary schools in Morazán, El Salvador. Students in this study received 90 minutes per week of additional math instruction (effectively nearly doubling total math instruction per week) through teacher-led regular lessons, teacher-assisted Khan Academy lessons, or similar lessons assisted by technical supervisors with no content expertise. (Importantly, the first group provided differentiated instruction, which is not the norm in Salvadorian schools). All three groups outperformed both schools without any additional lessons and classrooms without additional lessons in the same schools as the program. The teacher-assisted Khan Academy lessons performed 0.24 SDs better, the supervisor-led lessons 0.22 SDs better, and the teacher-led regular lessons 0.15 SDs better, but the authors could not determine whether the effects across versions were different.

Together, these studies suggest that instructional videos work best when provided as a complement to, rather than as a substitute for, regular instruction. Yet, the main limitation of these studies is the multifaceted nature of the Khan Academy portal, which also includes other components found to positively improve learner achievement, such as differentiated instruction by students’ learning levels. While the software does not provide the type of personalization discussed above, learners are asked to take a placement test and, based on their score, educators assign them different work. Therefore, it is not clear from these studies whether the effects from Khan Academy are driven by its instructional videos or to the software’s ability to provide differentiated activities when combined with placement tests.

Games and gamification

Technology can also increase learner engagement by presenting exercises as games and/or by encouraging learner to play and compete with others (e.g., using leaderboards and rewards)—an approach known as “gamification.” Both approaches can increase learner motivation and effort by presenting learners with entertaining opportunities for practice and by leveraging peers as commitment devices.

There are very few studies on the effects of games and gamification in low- and middle-income countries. Recently, Araya, Arias Ortiz, Bottan, and Cristia (2019) evaluated an initiative in which grade 4 students in Santiago, Chile were required to participate in two 90-minute sessions per week during the school day with instructional math software featuring individual and group competitions (e.g., tracking each learner’s standing in his/her class and tournaments between sections). After nine months, the program led to improvements of 0.27 SDs in the national student assessment in math (it had no spillover effects on reading). However, it had mixed effects on non-academic outcomes. Specifically, the program increased learners’ willingness to use computers to learn math, but, at the same time, increased their anxiety toward math and negatively impacted learners’ willingness to collaborate with peers. Finally, given that one of the weekly sessions replaced regular math instruction and the other one represented additional math instructional time, it is not clear whether the academic effects of the program are driven by the software or the additional time devoted to learning math.

The prognosis:

How can school systems adopt interventions that match their needs.

Here are five specific and sequential guidelines for decisionmakers to realize the potential of education technology to accelerate student learning.

1. Take stock of how your current schools, educators, and learners are engaging with technology .

Carry out a short in-school survey to understand the current practices and potential barriers to adoption of technology (we have included suggested survey instruments in the Appendices); use this information in your decisionmaking process. For example, we learned from conversations with current and former ministers of education from various developing regions that a common limitation to technology use is regulations that hold school leaders accountable for damages to or losses of devices. Another common barrier is lack of access to electricity and Internet, or even the availability of sufficient outlets for charging devices in classrooms. Understanding basic infrastructure and regulatory limitations to the use of education technology is a first necessary step. But addressing these limitations will not guarantee that introducing or expanding technology use will accelerate learning. The next steps are thus necessary.

“In Africa, the biggest limit is connectivity. Fiber is expensive, and we don’t have it everywhere. The continent is creating a digital divide between cities, where there is fiber, and the rural areas.  The [Ghanaian] administration put in schools offline/online technologies with books, assessment tools, and open source materials. In deploying this, we are finding that again, teachers are unfamiliar with it. And existing policies prohibit students to bring their own tablets or cell phones. The easiest way to do it would have been to let everyone bring their own device. But policies are against it.” H.E. Matthew Prempeh, Minister of Education of Ghana, on the need to understand the local context.

2. Consider how the introduction of technology may affect the interactions among learners, educators, and content .

Our review of the evidence indicates that technology may accelerate student learning when it is used to scale up access to quality content, facilitate differentiated instruction, increase opportunities for practice, or when it increases learner engagement. For example, will adding electronic whiteboards to classrooms facilitate access to more quality content or differentiated instruction? Or will these expensive boards be used in the same way as the old chalkboards? Will providing one device (laptop or tablet) to each learner facilitate access to more and better content, or offer students more opportunities to practice and learn? Solely introducing technology in classrooms without additional changes is unlikely to lead to improved learning and may be quite costly. If you cannot clearly identify how the interactions among the three key components of the instructional core (educators, learners, and content) may change after the introduction of technology, then it is probably not a good idea to make the investment. See Appendix A for guidance on the types of questions to ask.

3. Once decisionmakers have a clear idea of how education technology can help accelerate student learning in a specific context, it is important to define clear objectives and goals and establish ways to regularly assess progress and make course corrections in a timely manner .

For instance, is the education technology expected to ensure that learners in early grades excel in foundational skills—basic literacy and numeracy—by age 10? If so, will the technology provide quality reading and math materials, ample opportunities to practice, and engaging materials such as videos or games? Will educators be empowered to use these materials in new ways? And how will progress be measured and adjusted?

4. How this kind of reform is approached can matter immensely for its success.

It is easy to nod to issues of “implementation,” but that needs to be more than rhetorical. Keep in mind that good use of education technology requires thinking about how it will affect learners, educators, and parents. After all, giving learners digital devices will make no difference if they get broken, are stolen, or go unused. Classroom technologies only matter if educators feel comfortable putting them to work. Since good technology is generally about complementing or amplifying what educators and learners already do, it is almost always a mistake to mandate programs from on high. It is vital that technology be adopted with the input of educators and families and with attention to how it will be used. If technology goes unused or if educators use it ineffectually, the results will disappoint—no matter the virtuosity of the technology. Indeed, unused education technology can be an unnecessary expenditure for cash-strapped education systems. This is why surveying context, listening to voices in the field, examining how technology is used, and planning for course correction is essential.

5. It is essential to communicate with a range of stakeholders, including educators, school leaders, parents, and learners .

Technology can feel alien in schools, confuse parents and (especially) older educators, or become an alluring distraction. Good communication can help address all of these risks. Taking care to listen to educators and families can help ensure that programs are informed by their needs and concerns. At the same time, deliberately and consistently explaining what technology is and is not supposed to do, how it can be most effectively used, and the ways in which it can make it more likely that programs work as intended. For instance, if teachers fear that technology is intended to reduce the need for educators, they will tend to be hostile; if they believe that it is intended to assist them in their work, they will be more receptive. Absent effective communication, it is easy for programs to “fail” not because of the technology but because of how it was used. In short, past experience in rolling out education programs indicates that it is as important to have a strong intervention design as it is to have a solid plan to socialize it among stakeholders.

Beyond reopening: A leapfrog moment to transform education?

On September 14, the Center for Universal Education (CUE) will host a webinar to discuss strategies, including around the effective use of education technology, for ensuring resilient schools in the long term and to launch a new education technology playbook “Realizing the promise: How can education technology improve learning for all?”

file-pdf Full Playbook – Realizing the promise: How can education technology improve learning for all? file-pdf References file-pdf Appendix A – Instruments to assess availability and use of technology file-pdf Appendix B – List of reviewed studies file-pdf Appendix C – How may technology affect interactions among students, teachers, and content?

About the Authors

Alejandro j. ganimian, emiliana vegas, frederick m. hess.

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Learning vs. Training, what is the difference?

It’s important to understand the difference between learning and training. Of course, they are inextricably linked , but they are unique aspects of any educational process . Training is the giving of information and knowledge, through speech, the written word or other methods of demonstration in a manner that instructs the trainee. Learning is the process of absorbing that information in order to increase skills and abilities and make use of it under a variety of contexts.

Whatever the goals, the quality of the learning will rely largely on the quality of the training, and so the role of trainer is very important as it can have a huge effect on the outcome of a course for the learner.

Let’s look at the characteristics of each, and see what makes an e-learning environment work.

The characteristics of learning

As mentioned above, learning is the process of absorbing information and retaining it with the goal of increasing skills and abilities in order to achieve goals – but it’s more than that. Learning is what we go through when we want to be equipped for non-specific and unexpected situations and the two are not mutually exclusive. While you do learn to do something specific, you are also inadvertently equipped with the knowledge and/or skills to face future challenges. In essence, learning is all about equipping a person to tackle not just today’s issues, but preparing him/her to creatively come up with ways to tackle tomorrow’s issues .

The characteristics of training

Training, on the other hand, focuses more on the development of new skills or skill sets that will be used . Training is the process each new employee goes through when joining a company to learn how to carry out the day-to-day operations, know how their department works and how job-specific tools operate in order to carry out their responsibilities. In essence, through training, we are not looking to reshape the behavior of an individual rather the point is to teach the employee or learner how things are done so that they can then carry out a process on their own.

Ideally, an e-learning environment will utilize both learning and training principles throughout its curriculum. This allows instructors/trainers to provide their learners with the tools to tackle current issues, develop life-long skills, improve on their problem-solving skills and utilize resources to the best of their ability.

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Training Methods Essay

Introduction, methods of training, on the job training (ojt), lecture method, behavior modeling, reference list.

Training is a learning process where employees acquire knowledge and skills to improve their competence and to help them achieve organizational goals. Every organization will need to train its employees in order to increase their productivity and to become proficient and competent while carrying out their job duties.

There are two types of training methods: in-service training and pre-service training. Pre-service training is offered by formal institutions where persons attends regular classes in order to attain a formal diploma or degree whereas in-service training is undertaken when an organization offers time to time knowledge or skills upgrade to its staff.

Training methods are divided into cognitive methods and behavioral methods. Cognitive methods give information either in written form or orally and provides guidelines on how to go about job tasks. They include lectures, discussions, demonstrations, virtual reality, programmed instructions, computer based training and intelligent tutorial systems. On the other hand, behavioral methods enable learners to exercise behavior in real fashion and they help in skill development and attitude change.

Behavioral methods include games and simulations, behavioral modeling, case studies, equipment stimulators, business games, role plays and in-basket techniques. The training method selected should be able to motivate the learners to improve on their performance. In addition, the training should help staff transfer what they have learned in training to job situations and they should be allowed to actively participate during the training or learning period (Bass and Vaughan, 1966).

On job training, also known as hands on training, is the most commonly used training method in small organizations. This form of training always takes place at the actual work place.

The existing experienced and skilled managers and supervisors take the lead in training less experienced and knowledgeable employees who join or are already working in the organization. William and Kazanas (2004) mention that there is no official procedure for undertaking an OJT and point out that that trainers need not to have formal qualifications as long as they are experienced and have knowledge in the field.

The employees are coached, mentored, and instructed by their superiors on how to handle their job duties. This training method helps to identify weaknesses and strengths of the employees. However, the competence of this method cannot be proven because some sloppy work habits can be passed from supervisors to the trainees and this might affect their output.

Besides, finding the right time to implement training schedules can be a challenge because the trainers’ responsibilities might be left unattended to and this might affect the organizations’ performance. In spite of these limitations, this method is one of the most effective training techniques and has been successfully used in many organizations. Alipour, Salehi, and Shahnavaz (2009) avow that OJT results into more creativity, realization of organizational goals, and enhances work quality.

This method is considered the most effective training method because it targets a large number of individuals at a relatively low cost. Lectures can be conducted either in a formal or informal setting.

In an informal lecture, the audience actively participates while in a formal lecture, the subject matter is introduced by the instructor and he presents the main part of the lesson with little involvement of trainees.

This training method is relatively inexpensive as it reaches a large number of people. It can also be effective especially when the learners are involved. The instructor can deliver a lot of information to the learners in a short period of time.

Even though the lecture method has been considered the most appropriate training method, it has some drawbacks such as the inability to identify and correct misunderstandings among learners since he may not have full control of the audience. The effectiveness of this training method has been proven in numerous studies.

For instance, Burke and Day (1986) mention in their study that when interactive approaches such as quizzes, small group discussions, case studies, question cards, demonstrations and role playing, among others are used in lectures, it can be very effective in not only improving knowledge of the learners, but it might also enhance the learners’ interpersonal skills.

This training method has been acknowledged as the most suitable method for developing interpersonal skills. In this training technique, the learners observe what others are doing and learn how to perform similar tasks. For instance, they can watch a videotape and thereafter practice what they observed through role-plays or other types of simulation techniques. The underlying theory behind this training method is that once a person has watched the behavior shown in the video, he is likely to replicate it on job situations.

This method can be used in safety training, interviewee and interviewer training, sales training and interpersonal skills training. It reduces the time, costs of trial and error processes, and provides learners with opportunities to discover learning comfortably through practice (Decker &Nathan, 1985). Through practice by role-plays, the learners also develop interpersonal skills. This method has been found to be effective by in a number of studies according to Mayer & Russell (1987).

Alipour, M, Salehi, M, and Shahnavaz, A. (2009). A Study of on the Job Training Effectiveness: Empirical Evidence of Iran. International Journal of Business and Management , 4(11), 51-75.

Bass, B. M., and Vaughan, J. A. (1966). Training in industry: The management of learning . Belmont, CA: Wadsworth Publishing.

Burke, M J, and Day, RR. (1986). A cumulative study of the effectiveness of managerial training. Journal of Applied Psychology , 71(2), 232-245

Decker, P. & Nathan, B. (1985). B ehavior Modeling Training: Principles and Applications . New York: Praeger.

Mayer, S.J., and Russell, J.S. (1987). Behavior Modeling Training in Organizations: Concerns and Conclusions. Journal of Management Spring, 13(1), 21-40.

William J. R., and Kazanas, H.C. (2004). Improving On the Job Training , 2nd edition. San Francisco: Jossey-Bass.

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What Is Education For?

Read an excerpt from a new book by Sir Ken Robinson and Kate Robinson, which calls for redesigning education for the future.

What is education for? As it happens, people differ sharply on this question. It is what is known as an “essentially contested concept.” Like “democracy” and “justice,” “education” means different things to different people. Various factors can contribute to a person’s understanding of the purpose of education, including their background and circumstances. It is also inflected by how they view related issues such as ethnicity, gender, and social class. Still, not having an agreed-upon definition of education doesn’t mean we can’t discuss it or do anything about it.

We just need to be clear on terms. There are a few terms that are often confused or used interchangeably—“learning,” “education,” “training,” and “school”—but there are important differences between them. Learning is the process of acquiring new skills and understanding. Education is an organized system of learning. Training is a type of education that is focused on learning specific skills. A school is a community of learners: a group that comes together to learn with and from each other. It is vital that we differentiate these terms: children love to learn, they do it naturally; many have a hard time with education, and some have big problems with school.

There are many assumptions of compulsory education. One is that young people need to know, understand, and be able to do certain things that they most likely would not if they were left to their own devices. What these things are and how best to ensure students learn them are complicated and often controversial issues. Another assumption is that compulsory education is a preparation for what will come afterward, like getting a good job or going on to higher education.

So, what does it mean to be educated now? Well, I believe that education should expand our consciousness, capabilities, sensitivities, and cultural understanding. It should enlarge our worldview. As we all live in two worlds—the world within you that exists only because you do, and the world around you—the core purpose of education is to enable students to understand both worlds. In today’s climate, there is also a new and urgent challenge: to provide forms of education that engage young people with the global-economic issues of environmental well-being.

This core purpose of education can be broken down into four basic purposes.

Education should enable young people to engage with the world within them as well as the world around them. In Western cultures, there is a firm distinction between the two worlds, between thinking and feeling, objectivity and subjectivity. This distinction is misguided. There is a deep correlation between our experience of the world around us and how we feel. As we explored in the previous chapters, all individuals have unique strengths and weaknesses, outlooks and personalities. Students do not come in standard physical shapes, nor do their abilities and personalities. They all have their own aptitudes and dispositions and different ways of understanding things. Education is therefore deeply personal. It is about cultivating the minds and hearts of living people. Engaging them as individuals is at the heart of raising achievement.

The Universal Declaration of Human Rights emphasizes that “All human beings are born free and equal in dignity and rights,” and that “Education shall be directed to the full development of the human personality and to the strengthening of respect for human rights and fundamental freedoms.” Many of the deepest problems in current systems of education result from losing sight of this basic principle.

Schools should enable students to understand their own cultures and to respect the diversity of others. There are various definitions of culture, but in this context the most appropriate is “the values and forms of behavior that characterize different social groups.” To put it more bluntly, it is “the way we do things around here.” Education is one of the ways that communities pass on their values from one generation to the next. For some, education is a way of preserving a culture against outside influences. For others, it is a way of promoting cultural tolerance. As the world becomes more crowded and connected, it is becoming more complex culturally. Living respectfully with diversity is not just an ethical choice, it is a practical imperative.

There should be three cultural priorities for schools: to help students understand their own cultures, to understand other cultures, and to promote a sense of cultural tolerance and coexistence. The lives of all communities can be hugely enriched by celebrating their own cultures and the practices and traditions of other cultures.

Education should enable students to become economically responsible and independent. This is one of the reasons governments take such a keen interest in education: they know that an educated workforce is essential to creating economic prosperity. Leaders of the Industrial Revolution knew that education was critical to creating the types of workforce they required, too. But the world of work has changed so profoundly since then, and continues to do so at an ever-quickening pace. We know that many of the jobs of previous decades are disappearing and being rapidly replaced by contemporary counterparts. It is almost impossible to predict the direction of advancing technologies, and where they will take us.

How can schools prepare students to navigate this ever-changing economic landscape? They must connect students with their unique talents and interests, dissolve the division between academic and vocational programs, and foster practical partnerships between schools and the world of work, so that young people can experience working environments as part of their education, not simply when it is time for them to enter the labor market.

Education should enable young people to become active and compassionate citizens. We live in densely woven social systems. The benefits we derive from them depend on our working together to sustain them. The empowerment of individuals has to be balanced by practicing the values and responsibilities of collective life, and of democracy in particular. Our freedoms in democratic societies are not automatic. They come from centuries of struggle against tyranny and autocracy and those who foment sectarianism, hatred, and fear. Those struggles are far from over. As John Dewey observed, “Democracy has to be born anew every generation, and education is its midwife.”

For a democratic society to function, it depends upon the majority of its people to be active within the democratic process. In many democracies, this is increasingly not the case. Schools should engage students in becoming active, and proactive, democratic participants. An academic civics course will scratch the surface, but to nurture a deeply rooted respect for democracy, it is essential to give young people real-life democratic experiences long before they come of age to vote.

Eight Core Competencies

The conventional curriculum is based on a collection of separate subjects. These are prioritized according to beliefs around the limited understanding of intelligence we discussed in the previous chapter, as well as what is deemed to be important later in life. The idea of “subjects” suggests that each subject, whether mathematics, science, art, or language, stands completely separate from all the other subjects. This is problematic. Mathematics, for example, is not defined only by propositional knowledge; it is a combination of types of knowledge, including concepts, processes, and methods as well as propositional knowledge. This is also true of science, art, and languages, and of all other subjects. It is therefore much more useful to focus on the concept of disciplines rather than subjects.

Disciplines are fluid; they constantly merge and collaborate. In focusing on disciplines rather than subjects we can also explore the concept of interdisciplinary learning. This is a much more holistic approach that mirrors real life more closely—it is rare that activities outside of school are as clearly segregated as conventional curriculums suggest. A journalist writing an article, for example, must be able to call upon skills of conversation, deductive reasoning, literacy, and social sciences. A surgeon must understand the academic concept of the patient’s condition, as well as the practical application of the appropriate procedure. At least, we would certainly hope this is the case should we find ourselves being wheeled into surgery.

The concept of disciplines brings us to a better starting point when planning the curriculum, which is to ask what students should know and be able to do as a result of their education. The four purposes above suggest eight core competencies that, if properly integrated into education, will equip students who leave school to engage in the economic, cultural, social, and personal challenges they will inevitably face in their lives. These competencies are curiosity, creativity, criticism, communication, collaboration, compassion, composure, and citizenship. Rather than be triggered by age, they should be interwoven from the beginning of a student’s educational journey and nurtured throughout.

From Imagine If: Creating a Future for Us All by Sir Ken Robinson, Ph.D and Kate Robinson, published by Penguin Books, an imprint of Penguin Publishing Group, a division of Penguin Random House, LLC. Copyright © 2022 by the Estate of Sir Kenneth Robinson and Kate Robinson.

Essay on Skill Development

Students are often asked to write an essay on Skill Development in their schools and colleges. And if you’re also looking for the same, we have created 100-word, 250-word, and 500-word essays on the topic.

Let’s take a look…

100 Words Essay on Skill Development

Introduction.

Skill development is a vital part of personal growth. It involves learning new abilities or improving existing ones to enhance performance.

Importance of Skill Development

Skills are essential for success in life. They help us solve problems, work efficiently, and achieve our goals.

Types of Skills

There are many types of skills, such as communication, problem-solving, creativity, and teamwork. Each skill can be developed with practice.

In conclusion, skill development is a lifelong process. It equips us with the capabilities needed to navigate life effectively.

250 Words Essay on Skill Development

Introduction to skill development.

Skill development refers to the process of identifying one’s skill gaps and developing and honing these skills. It is vital for personal, professional, and economic growth. In an ever-evolving world, the ability to adapt and acquire new skills is crucial to meet industry demands and personal goals.

The Importance of Skill Development

Skill development is a tool to enhance both productivity and employability. It fosters adaptability, paving the way for lifelong learning and continuous improvement. In the professional sphere, developing skills can lead to career advancement and job security. On a macro level, it contributes to the economic development of a nation by improving the quality of its workforce.

Methods of Skill Development

Skill development can be achieved through various methods like education, training, and practical experience. Modern methods include e-learning platforms, which offer flexibility and a wide array of courses. Internships and on-the-job training are practical ways of acquiring industry-specific skills.

Challenges and Solutions

Despite its importance, skill development faces challenges like the rapid pace of technological change and a lack of awareness about the need for continuous learning. To overcome these, a mindset shift is required where learning is seen as a lifelong process. Governments and educational institutions need to promote skill development programs and provide access to quality training.

In conclusion, skill development is a vital aspect of personal and professional growth. By embracing lifelong learning and leveraging available resources, individuals can enhance their skills, adapt to changing environments, and contribute to societal progress. It is a shared responsibility between individuals, educational institutions, and governments to promote and support skill development.

500 Words Essay on Skill Development

Skill development refers to the process of identifying one’s skill gaps and developing and honing these skills. It is vital because the development of skills fosters employability and will help you navigate the rapidly changing work environment. In today’s age of digital disruption and constant innovation, skills such as critical thinking, creativity, and complex problem-solving are more valuable than ever.

The importance of skill development cannot be overstated. With the advent of the Fourth Industrial Revolution, the demand for new skill sets and competencies is increasing at an unprecedented rate. The World Economic Forum predicts that by 2025, 50% of all employees will need reskilling. Skill development is not just about acquiring new skills but also about enhancing existing ones and learning to adapt to a constantly evolving work environment.

Role of Education in Skill Development

Education plays a pivotal role in skill development. Traditional education systems, however, often fail to equip students with the necessary skills to navigate the modern workplace. It’s important for educational institutions to integrate skill development into their curriculums, focusing on skills like critical thinking, creativity, emotional intelligence, and digital literacy. It’s equally important for students to take charge of their own skill development, seeking out opportunities for learning beyond the classroom.

Skills for the Future

The future of work is uncertain and unpredictable due to rapid technological advancements. According to the World Economic Forum, the top skills for the future include complex problem-solving, critical thinking, creativity, people management, coordinating with others, emotional intelligence, judgement and decision making, service orientation, negotiation, and cognitive flexibility. These are the skills that will drive the future economy and determine individual success in the job market.

The Role of Governments and Corporations

Governments and corporations also have a significant role to play in skill development. Governments need to invest in education and training programs that equip citizens with the skills needed for the future. Corporations, on the other hand, need to invest in training and development programs for their employees, helping them stay relevant in their roles and adapt to changing job requirements.

In conclusion, skill development is an ongoing process that everyone must engage in to stay relevant in today’s fast-paced world. It requires a collective effort from individuals, educational institutions, corporations, and governments. By focusing on skill development, we can prepare ourselves for the future of work, fostering a workforce that’s adaptable, innovative, and ready for whatever comes next.

That’s it! I hope the essay helped you.

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Home — Essay Samples — Life — Training — The Importance of Training

The Importance of Training

• Categories: Training

About this sample

Words: 3253 |

17 min read

Published: Sep 20, 2018

Words: 3253 | Pages: 6 | 17 min read

Table of contents

Introduction, defining training, works cited, training cycle (this is a replication of page 4 of the framework of standards for magistrate training and development).

• Stage 1 – Identification of training needs This initial stage of the training cycle addresses finding out if there is, or identifying training needs. If a need is identified, it is at this stage that who needs trained (target audience), in what and how you will know the training has had the intended impact success criteria* of the training should be identified. This stage will help those who identify training needs to consider why the training is required and its expected outcome and impact. How you will measure if the training has met the original need i.e. brought about intended change in behavior, performance etc.
• Stage 2 – Design of training solutions This stage covers planning, design and development of magistrate training. It aims to ensure that a systematic and consistent approach is adopted for all training solutions. Training solutions cover face to face training and open and flexible learning including e-learning.
• Stage 3 – Delivery of training solutions This stage of the training cycle ensures that the delivery of the training is effective and provides opportunities for the learners to learn. This will involve choosing the most appropriate format for meeting training needs, and taking advantage of different training methods.
• Stage 4 – Application of training in the court environment This stage of the training cycle is concerned with ensuring that all learning outcomes are applied and reinforced in practice within the court environment. This stage will help those who monitor the development of individual learners and review their progress.
• Stage 5 – Evaluation of training solutions This stage of the training cycle deals with the collection, analysis and presentation of information to establish the improvement in performance that results from this. This stage will help those who evaluate learning programmes, or who respond to developments in learning, or plan and introduce improvements in learning interventions.

Staff Training

Importance of staff training, benefits of staff training for individual and team, benefits of training for organizations (astd-american society for training and development), benefits of training for the society, training methods, off-the-job training methods, computer based training, testing, evaluation, and follow-up, management role.

• ASTD-American Society for Training and Development. (n.d.). Benefits of Training. Retrieved from https://www.td.org/research/benefits-of-training
• Bellizzi, J. A., & Pointkowski, S. R. (1990). Training needs assessment in the hospitality industry. Journal of Management Development, 9(6), 24-31.
• Blanchard, P. N., & Thacker, J. W. (1998). Effective training: Systems, strategies, and practices. Prentice Hall.
• Goldstein, I. L., & Ford, J. K. (2002). Training in organizations: Needs assessment, development, and evaluation (4th ed.). Cengage Learning.
• Lee, M. (1991). Competitiveness, employee participation and training in the hotel industry: An international study. International Journal of Contemporary Hospitality Management, 3(2), 25-29.
• Martocchio, J. J., & Baldwin, T. T. (1997). The challenge of training and development. Journal of Applied Psychology, 82(2), 153-163.
• McClelland, D. C. (2002). Human motivation. Cambridge University Press.
• Nickson, D. (2007). Human resource management for the hospitality and tourism industries. Elsevier.
• Sommerville, I. (2007). Software engineering (8th ed.). Pearson Education.
• Swanson, R. A. (2001). The foundations of transfer of training. Advances in Developing Human Resources, 3(3), 261-271.

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What is a Training Essay?

A training essay is a documented experience for training. A student must have 45 hours of documented training for each credit she attempts to earn through a training essay. An example of this would be a student having 135 hours of training in one discipline (subject matter).

Professional Training, Licenses, and Certifications

Bay Path recognizes the workplace learning that receives an evaluation by the American Council on Education (ACE). Students can earn credit by writing a Training Essay that is typically 2 to 4 pages in length. Students can earn one credit for every 45 hours of documented training. Bay Path accepts ACE credit for military service.  In addition, and on a case-by-case basis, the institution will consider credit recommendations for experiences other than military service.

If you have taken any of the formal courses or examinations evaluated by this organization, Bay Path will accept the credit recommendations with the submission of an ACE credit transcript. Students should send ACE transcripts to the Associate Registrar for The American Women’s College at Bay Path University. Credits receive approval and denial according to the established practices of that office. NOTE : A maximum of 12 credits can transfer into a Bay Path degree program once a student matriculates.

Bay Path advises that students pursuing ACE credits should initiate this process prior to matriculation so that established transfer limits do not affect the acceptance of these credits. The following websites help students connect to ACE and identify possible ACE-approved training.

• National Guide to College Credit for Workforce Training http://www2.acenet.edu/credit/?fuseaction=browse.main
• College Credit for Military Service https://www.acenet.edu/Programs-Services/Pages/Credit-Transcripts/Military-Guide-Online.aspx

Prior Learning Assessment Handbook Copyright © by Bay Path University is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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What I’ve Learned From My Students’ College Essays

The genre is often maligned for being formulaic and melodramatic, but it’s more important than you think.

By Nell Freudenberger

Most high school seniors approach the college essay with dread. Either their upbringing hasn’t supplied them with several hundred words of adversity, or worse, they’re afraid that packaging the genuine trauma they’ve experienced is the only way to secure their future. The college counselor at the Brooklyn high school where I’m a writing tutor advises against trauma porn. “Keep it brief , ” she says, “and show how you rose above it.”

I started volunteering in New York City schools in my 20s, before I had kids of my own. At the time, I liked hanging out with teenagers, whom I sometimes had more interesting conversations with than I did my peers. Often I worked with students who spoke English as a second language or who used slang in their writing, and at first I was hung up on grammar. Should I correct any deviation from “standard English” to appeal to some Wizard of Oz behind the curtains of a college admissions office? Or should I encourage students to write the way they speak, in pursuit of an authentic voice, that most elusive of literary qualities?

In fact, I was missing the point. One of many lessons the students have taught me is to let the story dictate the voice of the essay. A few years ago, I worked with a boy who claimed to have nothing to write about. His life had been ordinary, he said; nothing had happened to him. I asked if he wanted to try writing about a family member, his favorite school subject, a summer job? He glanced at his phone, his posture and expression suggesting that he’d rather be anywhere but in front of a computer with me. “Hobbies?” I suggested, without much hope. He gave me a shy glance. “I like to box,” he said.

I’ve had this experience with reluctant writers again and again — when a topic clicks with a student, an essay can unfurl spontaneously. Of course the primary goal of a college essay is to help its author get an education that leads to a career. Changes in testing policies and financial aid have made applying to college more confusing than ever, but essays have remained basically the same. I would argue that they’re much more than an onerous task or rote exercise, and that unlike standardized tests they are infinitely variable and sometimes beautiful. College essays also provide an opportunity to learn precision, clarity and the process of working toward the truth through multiple revisions.

When a topic clicks with a student, an essay can unfurl spontaneously.

Even if writing doesn’t end up being fundamental to their future professions, students learn to choose language carefully and to be suspicious of the first words that come to mind. Especially now, as college students shoulder so much of the country’s ethical responsibility for war with their protest movement, essay writing teaches prospective students an increasingly urgent lesson: that choosing their own words over ready-made phrases is the only reliable way to ensure they’re thinking for themselves.

Teenagers are ideal writers for several reasons. They’re usually free of preconceptions about writing, and they tend not to use self-consciously ‘‘literary’’ language. They’re allergic to hypocrisy and are generally unfiltered: They overshare, ask personal questions and call you out for microaggressions as well as less egregious (but still mortifying) verbal errors, such as referring to weed as ‘‘pot.’’ Most important, they have yet to put down their best stories in a finished form.

I can imagine an essay taking a risk and distinguishing itself formally — a poem or a one-act play — but most kids use a more straightforward model: a hook followed by a narrative built around “small moments” that lead to a concluding lesson or aspiration for the future. I never get tired of working with students on these essays because each one is different, and the short, rigid form sometimes makes an emotional story even more powerful. Before I read Javier Zamora’s wrenching “Solito,” I worked with a student who had been transported by a coyote into the U.S. and was reunited with his mother in the parking lot of a big-box store. I don’t remember whether this essay focused on specific skills or coping mechanisms that he gained from his ordeal. I remember only the bliss of the parent-and-child reunion in that uninspiring setting. If I were making a case to an admissions officer, I would suggest that simply being able to convey that experience demonstrates the kind of resilience that any college should admire.

The essays that have stayed with me over the years don’t follow a pattern. There are some narratives on very predictable topics — living up to the expectations of immigrant parents, or suffering from depression in 2020 — that are moving because of the attention with which the student describes the experience. One girl determined to become an engineer while watching her father build furniture from scraps after work; a boy, grieving for his mother during lockdown, began taking pictures of the sky.

If, as Lorrie Moore said, “a short story is a love affair; a novel is a marriage,” what is a college essay? Every once in a while I sit down next to a student and start reading, and I have to suppress my excitement, because there on the Google Doc in front of me is a real writer’s voice. One of the first students I ever worked with wrote about falling in love with another girl in dance class, the absolute magic of watching her move and the terror in the conflict between her feelings and the instruction of her religious middle school. She made me think that college essays are less like love than limerence: one-sided, obsessive, idiosyncratic but profound, the first draft of the most personal story their writers will ever tell.

Nell Freudenberger’s novel “The Limits” was published by Knopf last month. She volunteers through the PEN America Writers in the Schools program.

The world is getting “smarter” every day, and to keep up with consumer expectations, companies are increasingly using machine learning algorithms to make things easier. You can see them in use in end-user devices (through face recognition for unlocking smartphones) or for detecting credit card fraud (like triggering alerts for unusual purchases).

Within  artificial intelligence  (AI) and  machine learning , there are two basic approaches: supervised learning and unsupervised learning. The main difference is that one uses labeled data to help predict outcomes, while the other does not. However, there are some nuances between the two approaches, and key areas in which one outperforms the other. This post clarifies the differences so you can choose the best approach for your situation.

Supervised learning  is a machine learning approach that’s defined by its use of labeled data sets. These data sets are designed to train or “supervise” algorithms into classifying data or predicting outcomes accurately. Using labeled inputs and outputs, the model can measure its accuracy and learn over time.

Supervised learning can be separated into two types of problems when  data mining : classification and regression:

• Classification  problems use an algorithm to accurately assign test data into specific categories, such as separating apples from oranges. Or, in the real world, supervised learning algorithms can be used to classify spam in a separate folder from your inbox. Linear classifiers, support vector machines, decision trees and  random forest  are all common types of classification algorithms.
• Regression  is another type of supervised learning method that uses an algorithm to understand the relationship between dependent and independent variables. Regression models are helpful for predicting numerical values based on different data points, such as sales revenue projections for a given business. Some popular regression algorithms are linear regression, logistic regression, and polynomial regression.

Unsupervised learning  uses machine learning algorithms to analyze and cluster unlabeled data sets. These algorithms discover hidden patterns in data without the need for human intervention (hence, they are “unsupervised”).

Unsupervised learning models are used for three main tasks: clustering, association and dimensionality reduction:

• Clustering  is a data mining technique for grouping unlabeled data based on their similarities or differences. For example, K-means clustering algorithms assign similar data points into groups, where the K value represents the size of the grouping and granularity. This technique is helpful for market segmentation, image compression, and so on.
• Association  is another type of unsupervised learning method that uses different rules to find relationships between variables in a given data set. These methods are frequently used for market basket analysis and recommendation engines, along the lines of “Customers Who Bought This Item Also Bought” recommendations.
• Dimensionality reduction  is a learning technique that is used when the number of features (or dimensions) in a given data set is too high. It reduces the number of data inputs to a manageable size while also preserving the data integrity. Often, this technique is used in the preprocessing data stage, such as when autoencoders remove noise from visual data to improve picture quality.

The main distinction between the two approaches is the use of labeled data sets. To put it simply, supervised learning uses labeled input and output data, while an unsupervised learning algorithm does not.

In supervised learning, the algorithm “learns” from the training data set by iteratively making predictions on the data and adjusting for the correct answer. While supervised learning models tend to be more accurate than unsupervised learning models, they require upfront human intervention to label the data appropriately. For example, a supervised learning model can predict how long your commute will be based on the time of day, weather conditions and so on. But first, you must train it to know that rainy weather extends the driving time.

Unsupervised learning models, in contrast, work on their own to discover the inherent structure of unlabeled data. Note that they still require some human intervention for validating output variables. For example, an unsupervised learning model can identify that online shoppers often purchase groups of products at the same time. However, a data analyst would need to validate that it makes sense for a recommendation engine to group baby clothes with an order of diapers, applesauce, and sippy cups.

• Goals:  In supervised learning, the goal is to predict outcomes for new data. You know up front the type of results to expect. With an unsupervised learning algorithm, the goal is to get insights from large volumes of new data. The machine learning itself determines what is different or interesting from the data set.
• Applications: Supervised learning models are ideal for spam detection, sentiment analysis, weather forecasting and pricing predictions, among other things. In contrast, unsupervised learning is a great fit for anomaly detection, recommendation engines, customer personas and medical imaging.
• Complexity:  Supervised learning is a simple method for machine learning, typically calculated by using programs like R or Python. In unsupervised learning, you need powerful tools for working with large amounts of unclassified data. Unsupervised learning models are computationally complex because they need a large training set to produce intended outcomes.
• Drawbacks: Supervised learning models can be time-consuming to train, and the labels for input and output variables require expertise. Meanwhile, unsupervised learning methods can have wildly inaccurate results unless you have human intervention to validate the output variables.

Choosing the right approach for your situation depends on how your data scientists assess the structure and volume of your data, as well as the use case. To make your decision, be sure to do the following:

• Evaluate your input data:  Is it labeled or unlabeled data? Do you have experts that can support extra labeling?
• Define your goals:  Do you have a recurring, well-defined problem to solve? Or will the algorithm need to predict new problems?
• Review your options for algorithms:  Are there algorithms with the same dimensionality that you need (number of features, attributes, or characteristics)? Can they support your data volume and structure?

Classifying big data can be a real challenge in supervised learning, but the results are highly accurate and trustworthy. In contrast, unsupervised learning can handle large volumes of data in real time. But, there’s a lack of transparency into how data is clustered and a higher risk of inaccurate results. This is where semi-supervised learning comes in.

Can’t decide on whether to use supervised or unsupervised learning?  Semi-supervised learning  is a happy medium, where you use a training data set with both labeled and unlabeled data. It’s particularly useful when it’s difficult to extract relevant features from data—and when you have a high volume of data.

Semi-supervised learning is ideal for medical images, where a small amount of training data can lead to a significant improvement in accuracy. For example, a radiologist can label a small subset of CT scans for tumors or diseases so the machine can more accurately predict which patients might require more medical attention.

Machine learning models are a powerful way to gain the data insights that improve our world. To learn more about the specific algorithms that are used with supervised and unsupervised learning, we encourage you to delve into the Learn Hub articles on these techniques. We also recommend checking out the blog post that goes a step further, with a detailed look at deep learning and neural networks.

• What is Supervised Learning?
• What is Unsupervised Learning?
• AI vs. Machine Learning vs. Deep Learning vs. Neural Networks: What’s the difference?

To learn more about how to build machine learning models, explore the free tutorials on the  IBM® Developer Hub .

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Training language models to follow instructions with human feedback

Part of Advances in Neural Information Processing Systems 35 (NeurIPS 2022) Main Conference Track

Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul F. Christiano, Jan Leike, Ryan Lowe

Making language models bigger does not inherently make them better at following a user's intent. For example, large language models can generate outputs that are untruthful, toxic, or simply not helpful to the user. In other words, these models are not aligned with their users. In this paper, we show an avenue for aligning language models with user intent on a wide range of tasks by fine-tuning with human feedback. Starting with a set of labeler-written prompts and prompts submitted through a language model API, we collect a dataset of labeler demonstrations of the desired model behavior, which we use to fine-tune GPT-3 using supervised learning. We then collect a dataset of rankings of model outputs, which we use to further fine-tune this supervised model using reinforcement learning from human feedback. We call the resulting models InstructGPT. In human evaluations on our prompt distribution, outputs from the 1.3B parameter InstructGPT model are preferred to outputs from the 175B GPT-3, despite having 100x fewer parameters. Moreover, InstructGPT models show improvements in truthfulness and reductions in toxic output generation while having minimal performance regressions on public NLP datasets. Even though InstructGPT still makes simple mistakes, our results show that fine-tuning with human feedback is a promising direction for aligning language models with human intent.

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Key Advantages of Federated Learning

• Enhanced Privacy: Federated learning significantly reduces the risk of data breaches and misuse by keeping data on local devices. Sensitive information never leaves the device, ensuring user privacy is maintained.
• Improved Security: Since raw data is not transmitted over the network, the attack surface for potential breaches is minimized. Federated learning can incorporate secure aggregation techniques to protect model updates from being intercepted and reverse-engineered.
• Scalability: Federated learning leverages the computational power of edge devices, reducing the need for large-scale centralized infrastructure. This decentralized approach allows for scalable AI solutions that can operate efficiently across vast networks of devices.

Recent Advances in Federated Learning

• Local model training on each device and periodic averaging of model parameters across devices.
• Balances computational load and communication overhead.
• Secure aggregation protocols.
• Ensure model updates are aggregated without revealing individual updates.
• Use cryptographic methods for enhanced privacy and security.
• Methods proposed to handle data heterogeneity.
• Data sharing strategies and personalized federated learning approaches.
• Model compression techniques to reduce communication costs.

Applications of Federated Learning

• Collaborative medical research without compromising patient confidentiality.
• Example: Brain tumor segmentation across multiple hospitals without sharing patient data.
• Development of robust fraud detection systems while preserving user privacy.
• Financial institutions collaboratively train models on transaction data.
• Improvement of predictive text and personalized recommendations on smartphones.
• Models trained locally on user devices, maintaining privacy.
• Enhancing the capabilities of interconnected devices.
• Example: Smart home systems that learn user preferences locally.

Challenges for Federated Learning

Despite its advantages, federated learning faces several challenges that must be addressed for wider adoption. One of the primary challenges is the issue of non-IID (independent and identically distributed) data. In real-world scenarios, data across devices can be highly heterogeneous, which complicates the training process and may lead to biased models. Researchers have proposed methods to address data heterogeneity, such as data-sharing strategies and personalized federated learning approaches.

Another challenge is the high communication cost associated with transmitting model updates. Efficient communication protocols and model compression techniques are essential to mitigate this issue & ensure the feasibility of federated learning in resource-constrained environments. The integration of federated learning with other emerging technologies holds great potential. For instance, combining FL with blockchain can enhance security and transparency in decentralized AI systems. 5G networks will provide the bandwidth & low latency to support large-scale federated learning deployments.

Federated learning represents a paradigm shift in AI, offering a decentralized approach that enhances privacy and security. FL addresses critical concerns associated with traditional AI methods by enabling collaborative model training without centralized data collection. Despite the challenges, ongoing research paves the way for the broader adoption of federated learning across various industries. As this field continues to evolve, federated learning has the potential to become a cornerstone of secure and privacy-preserving AI systems.

• https://arxiv.org/abs/1806.00582
• https://arxiv.org/abs/1610.05492
• http://proceedings.mlr.press/v54/mcmahan17a.html
• https://dl.acm.org/doi/10.1145/3133956.3133982
• https://link.springer.com/chapter/10.1007/978-3-030-46640-4_34

Aswin AK is a consulting intern at MarkTechPost. He is pursuing his Dual Degree at the Indian Institute of Technology, Kharagpur. He is passionate about data science and machine learning, bringing a strong academic background and hands-on experience in solving real-life cross-domain challenges.

• OpenRLHF: An Open-Source AI Framework Enabling Efficient Reinforcement Learning from Human Feedback RLHF Scaling
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• TRANSMI: A Machine Learning Framework to Create Baseline Models Adapted for Transliterated Data from Existing Multilingual Pretrained Language Models mPLMs without Any Training
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