research paper about online platforms

• Research article
• Open access
• Published: 02 October 2020

Development of a new model on utilizing online learning platforms to improve students’ academic achievements and satisfaction

• Hassan Abuhassna   ORCID: orcid.org/0000-0002-5774-3652 1 ,
• Waleed Mugahed Al-Rahmi 1 ,
• Noraffandy Yahya 1 ,
• Megat Aman Zahiri Megat Zakaria 1 ,
• Azlina Bt. Mohd Kosnin 1 &
• Mohamad Darwish 2  

International Journal of Educational Technology in Higher Education volume  17 , Article number:  38 ( 2020 ) Cite this article

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This research aims to explore and investigate potential factors influencing students’ academic achievements and satisfaction with using online learning platforms. This study was constructed based on Transactional Distance Theory (TDT) and Bloom’s Taxonomy Theory (BTT). This study was conducted on 243 students using online learning platforms in higher education. This research utilized a quantitative research method. The model of this research illustrates eleven factors on using online learning platforms to improve students’ academic achievements and satisfaction. The findings showed that the students’ background, experience, collaborations, interactions, and autonomy positively affected students’ satisfaction. Moreover, effects of the students’ application, remembering, understanding, analyzing, and satisfaction was positively aligned with students’ academic achievements. Consequently, the empirical findings present a strong support to the integrative association between TDT and BTT theories in relation to using online learning platforms to improve students’ academic achievements and satisfaction, which could help decision makers in universities and higher education and colleges to plan, evaluate, and implement online learning platforms in their institutions.

Introduction

Higher education organizations over the previous two decades have offered full courses online as an integral part of their curricula, besides encouraging the completion throughout the online courses. Additionally, the number of students who are not participating in any courses online has continued to drop over the past few years. Similarly, it is perfectly possible to state that learning online is obviously an educational platform (Allen, Seaman, Poulin, & Straut, 2016 ). Courses online are trying to connect social networking components, experts’ content, because online resources are growing on daily basis. Such courses depend on active participation of a significant number of learners who participate independently in accordance with their education objectives, skills, and previous background and experience (McAuley, Stewart, Siemens, & Cormier, 2010 ). Nevertheless, learners differ in their previous background and experience, along with their education techniques, which clearly influence their online courses results besides their achievement (Kauffman, 2015 ). Consequently, despite the online learning evolution, learning online possibly will not be appropriate for each learner (Bouhnik & Carmi, 2013 ). Nevertheless, while online learning application among academic world has grown rapidly, not enough is identified regarding learners’ previous background and experience in learning online. Not so long ago, investigation concentrated on particular characteristics of learners’ experiences along with beliefs, for instance collaboration with their own instructor, online course quality, or studying with a certain learning management system (LMS) (Alexander & Golja, 2007 ; (Lester & King, 2009 ). Generally, limited courses or a single institution were investigated (Coates, James, & Baldwin, 2005 ; Lee, Yoon, & Lee, 2009 ). Few studies examined bigger sample sizes between one or more particular institutes (Alexander & Golja, 2007 ). Additionally, there is a shortage of researches that examine learners’ previous background and experience comparing face-to-face along with learning online elements, e.g., (Bliuc, Goodyear, & Ellis, 2007 ). The development of learners’ previous background and experience, skills, are realized to be the major advantages for administrative level for learning online.

Similarly, learners’ satisfaction and academic achievement towards learning online attracted considerable attention from scholars who employed several theoretical models in order to evaluate learners’ satisfaction and academic achievements (Abuhassna, Megat, Yahaya, Azlina, & Al-rahmi, 2020 ; Abuhassna & Yahaya, 2018 ; Al-Rahmi, Othman, & Yusuf, 2015a ; Al-Rahmi, Othman, & Yusuf, 2015b ). This present study highlights the effects of online learning platforms on student’s satisfaction, in relation to their background and prior experiences towards online learning platforms to identify learners that are going to be satisfied toward online course. Furthermore, this research explores the effects of transactional distance theory (TDT); student collaboration, student- instructor dialogue or communication, and student autonomy in relation to their satisfaction. Accordingly, this study investigates students’ academic achievements within online platforms, utilizing Bloom theory to measure students’ achievements through four main components, namely, understanding, remembering, applying, and analyzing. This study could have a significant influence on online course design and development. Additionally, this research may influence not only academic online courses but then other educational organizations according to the fact that several organizations offer training courses and solutions online. Both researchers and Instructors will be able to utilize and elaborate in accordance with the preliminary model, which was developed throughout this research, on the effects of online platforms on student’s satisfaction and academic achievements. Advantages of online learning and along with its applications were mentioned in earlier correlated literature (Abuhassna et al., 2020 ;Abuhassna & Yahaya, 2018 ; Al-Rahmi et al., 2018 ). However, despite the growing usage of online platforms, there is a shortage of employing this technology, which creates an issue in itself (Abuhassna & Yahaya, 2018 ; Al-Rahmi et al., 2018 ). Consequently, the research problem lies in the point that a model needs to be created to locate the significant evidence based on the data of student’s background, experiences and interactions within online learning environments which influence their academic performance and satisfaction. Thus, this developed model must be as a guidance for instructors and decision makers in the online education industry in terms of using online platforms to improve students learning experience through online platforms. Bearing in mind these conditions, our major problem was: how could we enhance students online learning experience in relation to both their academic achievements and satisfaction?

Research questions

The major research question that are anticipated to be answered is:

how could we enhance students online learning experience in relation to both their academic achievements and satisfaction?

To be able to answer this question, it is required to examine numerous sub-questions which have been stated as follow:

Q1: What is the relationship between students’ background and students’ satisfaction?

Q2: What is the relationship between students’ experience and students’ satisfaction?

Q3: What is the relationship between students’ collaboration and students’ satisfaction?

Q4: What is the relationship between students’ interaction and students’ satisfaction?

Q5: What is the relationship between students’ autonomy and students’ satisfaction?

Q6: What is the relationship between students’ satisfaction and students’ academic achievements?

Q7: What is the relationship between students’ application and students’ academic achievements?

Q8: What is the relationship between students’ remembering and students’ academic achievements?

Q9: What is the relationship between students’ understanding and students’ academic achievements?

Q10: What is the relationship between students’ analyzing and students’ academic achievements?

Research theory and hypotheses development

When designing web-courses within online learning instructions or mechanisms in general, educators are left with several decisions and considerations to face, which accordingly affect how students experience instruction, how they construct and process knowledge, how students could be satisfied through this experiment, and how web-based learning courses could enhance their academic achievements. In this study, we construct our theoretical framework according to Moore transactional distance theory (TDT) to measure student’s satisfaction, in addition to Bloom theory components to measure students’ academic achievements. Though the origins of TDT can be traced to the work of Dewey, it is Michael Moore who is identified as the innovator of this theory that first appeared in 1972. In his study and development of the theory, he acknowledged three main components of TDT that work as the base for much of the research on DL. Also, Bloom’s Taxonomy was established in 1956 under the direction of educational psychologist to measure students’ academic achievement (Bloom, Engelhart, Furst, Hill, & Krathwohl, 1956 ). TDT theory has been selected in this study since Transactional distance’s term indicates the geographical space between the student and instructor. Based on the learning understanding, which happens through learner’s interaction with his environment. This theory considers the role of each of these elements (Student’s autonomy, Dialogue, and class structure) whereas these three elements could help to investigate student’s satisfaction. Moore’s ( 1990 ) notion of ‘Transactional Distance’ adopt the distance that happens in all relations in education. The distance in the theory is mainly specified the dialogue’s amount which happens between the student and the teacher, and the structure’s amount in the course design. Which serves the main goal of this study as to enhance students online learning experience in relation to their satisfaction. Whereas, Bloom Theory has been selected in this study in addition to TDT to enhance students online learning experience in relation to their student’s achievements. In a conclusion both methods were implemented to develop and hypothesis this study hypothesis. See Fig.  1 .

Research Model and Hypotheses

Hypothesis of the study

H1: There is a significant relationship between students’ background and students’ satisfaction.

H2: There is a significant relationship between students’ experience and students’ satisfaction.

H3: There is a significant relationship between students’ collaboration and students’ satisfaction.

H4: There is a significant relationship between students’ interaction and students’ satisfaction.

H5: There is a significant relationship between students’ autonomy and students’ satisfaction.

H6: There is a significant relationship between students’ satisfaction and students’ academic achievements.

H7: There is a significant relationship between students’ application and students’ academic achievements.

H8: There is a significant relationship between students’ remembering and students’ academic achievements.

H9: There is a significant relationship between students’ understanding and students’ academic achievements.

H10: There is a significant relationship between students’ analyzing and students’ academic achievements.

Hypothesis developments and literature review

This Section of the study will discuss the study hypothesis and relates each hypothesis to its related studies from the literature.

Students background toward online platforms

Students’ background regarding online platforms in this study is referred to as their readiness and willingness to use and adapt to different online platforms, providing them with the needed support and assistance. Students’ background towards online learning is a crucial component throughout this process, as prior research revealed that there are implementation issues, for instance; the deficiency of qualified lecturers, infrastructure and facilities, in addition to students’ readiness, besides students’ resistance to accept online learning platforms in addition to the Learning Management System (LMS) platforms, as educational tools (Azhari & Ming, 2015 ). However, student demand continued to increase, spreading to global audiences due to its exceptional functionality, flexibility and eventual accessibility (Azhari & Ming, 2015 ). There have been persistent apprehensions regarding online learning quality compared with traditional learning settings. In their research, (Paechter & Maier, 2010 ; Panyajamorn, Suthathip, Kohda, Chongphaisal, & Supnithi, 2018 ) have discovered that Austrian learners continue to prefer traditional learning environments due to communication goals, along with the interpersonal relations preservation. Moreover, (Lau & Shaikh, 2012 ) have discovered that Malaysian learners’ internet efficiency and computer skills, along with their personal demographics like gender, background, level of the study, as well as their financial income lead to a significant difference in their readiness towards online learning platforms. Abuhassna and Yahaya ( 2018 ) claimed that the current technologies in education play an essential role in providing a full online learning experience which is close enough to a face-to-face class in spite of the physical separation of the students from their educator, along with other students. Platforms of online learning lend themselves towards a less hierarchical methodology in education, fulfilling the learning desires of individuals which do not approach new information in a linear or a systematic manner. Platforms of online learning additionally are the most suitable ways for autonomous students (Abuhassna et al., 2020 ; Abuhassna & Yahaya, 2018 ; Paechter & Maier, 2010 ; Panyajamorn et al., 2018 ).

Students experience toward online platforms

Students’ experience in the current research indicates that learners must have prior experience in relation to utilizing online learning platform in their education settings. Thus, students experience towards online learning offers several advantages among themselves and their instructors in strengthening students’ learning experiences especially for isolated learners (Jaques & Salmon, 2007 ; Lau & Shaikh, 2012 ; Salmon, 2011 ; Salmon, 2014 ). Regardless of student recognition of the advantages towards supporting their learning throughout utilizing the technology, difficulties may occur through the boundaries about their technical capabilities and prior experiences towards utilizing the software itself from the perspective of its functionality. As demonstrated over learner’s experience and feedback from several online sessions over the years, this may frequently become a frustration source between both learners and their instructors, as this may make typically uncomplicated duties, for instance, watching a video, uploading a document, and other simple tasks to be progressively complicated for them, having no such prior experience. Furthermore, when filling out evaluations, for instance, online group presentations, the relatively limited capability to communicate face-to-face then to rely on a non-verbal signal along with audience’s body language might be a discouraging component. Nonetheless, the significance of being in a position to participate with other colleagues employing online sessions, which are occasionally nonvisual, for instance; teleconference format is a progressively significant skill in the modern workplace, thus affirming the importance of concise, clear, intensive interactions skills (Salmon, 2011 ; Salmon, 2014 ).

Student collaboration among themselves in online platforms

Students’ collaborations in the current study refers to the communication and feedback among themselves in online platforms. To refine and measure transactional distance using a survey tool, (Rabinovich, 2009 ) created a survey instrument to measure transactional distance in a higher education setting. A survey was sent to 235 students enrolled in a synchronous web-based graduate class in business regarding transactional distance and Collaborations (Rabinovich, 2009 ). The synchronous learning environment was described as a place where “live on-campus classes are conveyed simultaneously to both in-class students on campus and remote students on the Web who join via virtual classroom Web collaboration software” (Rabinovich, 2009 ). The virtual classroom software is similar to the characteristics of the two different software described by (Falloon, 2011 ; Mathieson, 2012 ) that it allows for students to interact with the educator and fellow students in real-time (Rabinovich, 2009 ). Moreover, (Kassandrinou, Angelaki, & Mavroidis, 2014 ) reported that the instructor plays a crucial role as interaction and communication helpers, as they are tasked with fostering, reassuring and assisting communication and interaction among students. Face-to-face tutorials have proven to be a vast opportunity for a multitude of students to interchange ideas, argue the content of the course and its related concerns (Vasala & Andreadou, 2010 ).

Students’ interactions with the instructor in online platforms

Purposeful interaction or (dialogue) in the current study describes communication that is learner-learner and learner-instructor which is designed to improve the understanding of the student. According to (Shearer, 2010 ) communication should also be constructive in that it builds upon ideas and work from others, as well as assists others in learning. (Moore, 1972 ) affirmed that learners also must realize that, and value the importance of the learning interactions as a vital part of the learning process. In a manner similar to (Benson & Samarawickrema, 2009 ] study of teacher preparatory students, (Falloon, 2011 ) investigated the use of digital tools in a case study at a teacher education program in New Zealand. (Mathieson, 2012 ) also explored the role dialogue plays in digital learning environments. She created a digital survey that examined students’ perception of audio-visual feedback in courses that utilize screen casting digital tools. (Moore, 2007 ) discusses autonomous learners searching for courses that do not stress structure and dialogue in order explain and enhance their learning progression. (Abuhassna et al., 2020 ; Abuhassna & Yahaya, 2018 ; Al-Rahmi et al., 2015b ; Al-Rahmi, Othman, & Yusuf, 2015d ; Furnborough, 2012 ) concluded that the feeling of cooperation that learners’ share with their fellow students effect their reaction concerning their collaboration with their peers.

Student autonomy in online platforms

Student autonomy in the current study refers to their independence and motivation towards learning. The learner is the motivation of the way toward learning, along with their expectations and requirements, thinking about everyone as a unique individual and hence investigating their own capacities and possibilities. Thus, extraordinary importance is attributed to autonomy in DL environments, since the option of instructive intercession offered in distance education empowers students towards learning autonomy (Massimo, 2014 ). In this respect, the connection between autonomy of student and explicit parts of the learning procedure are in the center of consideration as mentioned. (Madjar, Nave, & Hen, 2013 ) concluded that a learners’ autonomy-supportive environment provides these learners with adoption of a more aims guided learning, leading to more learning achievements. This is why autonomy is desired in the online settings for both individual development and greater achievement in academic environments. The researchers also indicate in their research that while autonomy supports outcomes in goals and aims guiding, educator practices mainly lead to goals which necessary cannot adapt. Thus, supportive-autonomy learning process needs to be designed with affective elements consideration as well. However, (Stroet, Opdenakker, & Minnaert, 2013 ) efficiently surveyed 71 experimental studies on the impacts of autonomy supportive teaching on motivation of learner and discovered a clear positive correlation. Similar to attribution theory, the relationship between learner control and inspiration involves the possibility of learners adjusting their own inspirations, for example, learners may be competent to change self-determined extrinsic motivation to intrinsic motivation. However, (Jacobs, Renandya, & Power, 2016 ) further indicated that learners will not reach the same level of autonomy without reviewing learner’s autonomy insights, reflecting on their learning experiences, sharing these experiences and reflections with other learners, and realizing the elements influencing all these processes, and the process of learning as well.

Student satisfaction in online platforms

Student satisfaction in the current study refers to the fact that there are many factors that play a role in determining the learner’s satisfaction, such as faculty, institution, individual learner element, interaction/communication elements, the course elements, and learning environment. Discussion of the elements also related to the role of the instructor, with the learner’s attitude, social presence, usefulness, and effectiveness of Online Platforms. (Yu, 2015 ) investigated that student satisfaction was positively associated with interaction, self-efficacy and self-regulation without significant gender variations. (Choy & Quek, 2016 ). examined the relationships between the learners’ perceived teaching, social, and cognitive element. In addition, satisfaction, academic performance, and achievement can be measured using a revised form of the survey instrument. (Kirmizi, 2014 ) studied connection between 6 psychosocial scales: personal relevance, educator assistance, student interaction and collaboration, student autonomy, authentic learning, along with active learning. A moderate level of correlation was found between these mentioned variables. Learner satisfaction predictors were educator support, personal relevance and authentic learning, while authentic learning was the only academic success predictor. Findings of (Bordelon, 2013 ) determined and described a positive correlation between both achievement and satisfaction. He demonstrated that the reasons behind these conclusions could be cultural variations in learner’s satisfaction which point out learning accession Zhu ( 2012 ). Scholars in the field of student satisfaction emphasis on the delivery besides the operational side of the student’s experience in the teaching process (Al-Rahmi, Othman, & Yusuf, 2015e ).

Students’ academic achievements in online platforms

Students achievements in this study refers to Bloom’s main four components of achievements, which are remembering, understanding, applying, and analyzing. Finding in a study conducted by (Whitmer, 2013 ) revealed the relationships between student academic achievement and the LMS usage, thus the findings showed a highly systematic association ( p  < .0000) in relation to every variable. These variables described 12% and 23% of variations within the final course marks, which indicates that learners who employed the LMS more often obtained higher marks than the others. Thus, the correlation techniques examined these variables separately to ascertain their association with the final mark. Moreover, it is not the technology itself; it is the educational methods in relation to which technology has been utilized that create a change in learners’ achievement. Instruments used are significant in identifying the technology impact, moreover, it is the implementation of those instruments under specific activities and for certain purposes which indicates whether or not they are effective. In contrast, a study conducted by (Barkand, 2017 ) revealed that LMS tools were not considered to have an effect on semester final grades when categorized by school year. In his study, semester final grades were a measure of student achievement, which has subjective elements. To account for the subjective elements in semester final grades, the study also included objective post test scores to evaluate student learning. Additionally, in this study, we refer to Bloom’s Taxonomy established in 1956 under the direction of educational psychologist for measuring students’ academic achievement (Bloom et al., 1956 ). Moreover, in this study, we selected fours domains of Blooms Taxonomy in order to achieve this study objectives, which are; application: which refers to using a concept in new context, for instance; applying what has been learned inside the classroom into different circumstances; remembering, which refers to recalling or retrieving prior learned knowledge; understanding, which refers to realizing the meaning, then clarification of problems instructions; analyzing, which refers to separating concepts or material into parts in such a way that its structure can be distinguished, understood among inferences and facts.

Students’ application

Applying involves “carrying out or using a procedure through executing or implementing” (Anderson & Krathwohl, 2001 ). Applying in this study refers to the student’s ability to use online platforms, such as how to log in, how to end session, how to download materials, how to access links and videos. Students can exchange information about a specific topic in online platforms such as Moodle, Google Documents, Wikis and apply knowledge to create and participate in online platforms.

Students’ remembering

Remembering is defined as “retrieving, recognizing, and recalling relevant knowledge from long-term memory” (Anderson & Krathwohl, 2001 ). In this study, remembering is referred to the ability to organize and remember online resources to easily find information on the internet. Moreover, students can easily cooperate with their colleagues and educator, contributing to the educational process and justifying their study procedure. Anderson and Krathwohl ( 2001 ) In their review of Bloom’s taxonomy, Anderson and Krathwohl ( 2001 ) recognized greater learning levels as creating, evaluating, and analyzing, with the lower learning levels as applying, understanding, and remembering.

Students’ understanding

Understanding involves “constructing meaning from oral, written, and graphic messages through interpreting, exemplifying, classifying, summarizing, inferring, comparing, and explaining” (Anderson & Krathwohl, 2001 ). In this study, understanding is referred to as understanding regarding a subject then putting forward new suggestions about online settings, for instance; understanding how e-learning works, or LMS. For example, students use online platforms to review concepts, courses, and prominent resources are being used inside the classroom environment.

Students’ analyzing

Analyzing includes “breaking material into constituent parts, determining how the parts relate to one another and to an overall structure or purpose through differentiating, organizing, and attributing” (Anderson & Krathwohl, 2001 ). Analyzing refers to the student’s ability to connect, discuss, mark-up, then evaluate the information received into one certain workplace or playground. Solomon and Schrum ( 2010 ) claim that educators have started employing online platforms for a range of activities, since they have become more familiar and there are ways for learners to benefit from using them. Generally, the purpose and goal are to publicize the development types, innovation, as well as additional activities that their learners usually do independently. Such instruments have also provided instructors ways to encourage and promote genuine cooperation in their project’s development (Solomon & Schrum, 2010 ).

Research methodology

A quantitative approach was implemented in this study to provide an inclusive insight in relation to students online learning experience and how to enhance both their satisfaction and academic achievements using a questionnaire. Two experts were referred for the evaluation of the questionnaire’s content. Before the collection of the data, permission regarding the current research purpose has been obtained from Universiti Teknologi Malaysia (UTM). In relation to the sampling and population, this research was conducted among undergraduate learners who have been online learning users. Learners, who had manually obtained the questionnaires, have been requested to fill in their details, then fill their own assessments regarding online learning platforms and its effects towards their academic achievements. Thus, for data analysis, the data that were attained from questionnaires were then analyzed using the Statistical Package for the Social Sciences (SPSS). Specifically, Structural Equation Modeling (SEM- Amos), which has been employed as a primary data analysis tool. Moreover, utilizing SEM-Amos process involves two main phases: evaluating construct validity, the convergent validity, along with the discriminant validity of the measurements; then analyzing the structural model. These mentioned two phases followed the recommendations of (Bagozzi, Yi, & Nassen, 1998; Hair, Sarstedt, Ringle, & Mena, 2012a , 2012b ).

Sample characteristics and data collection

A total of 283 questionnaires were distributed manually; of these, only 264, which make up 93.3% of the total number, were returned to the authors. Excluding the 26 incomplete questionnaires, 264 were evaluated employing SPSS. A total of 21 questionnaires have been excluded: 14 were incomplete and 7 having outliners. Thus, the overall number of valid questionnaires was 243 following this exclusion. This exclusion step is being supported by Hair et al. ( 2012a , 2012b ) . Moreover, Venkatesh, Thong, & Xu, 2012 who pointed out that this procedure is essential to be implemented as the existence of outliers could be a reason for inaccurate results. Regarding the respondent’s demographic details: 91 (37.4%) were males, and 152 (62.6%) were females. 149 (61.3%) were in the age range of 18 t0 20 years old, 77 (31.7%) were in the age range of 21 to 24 years old, and 17 (7.0%) were in the age range of 25 to 29 years old. Regarding level of study: 63 (25.9%) were from level 1, 72 (29.6%) were from level 2, 50 (20.6%) were from level 3, and 58 (23.9%) were from level 4.

Measurement instruments

The questionnaire in this study has been developed to fit the study hypothesis. Consequently, it was developed based into both theories that have been utilized in this study. The questionnaire has two main sections, first section aims to measure student satisfaction which is based on the TDT theory variables. Second section of the questionnaire has been developed to measure students’ academic achievement based on Bloom theory. According to Bloom theory there are four variables that measure students’ achievements, which are application, remembering, understanding, analyzing. On that basis the questionnaire has been developed to measure both students’ satisfaction and academic achievements . The construct items were adapted to ensure content validity. This questionnaire consisted of two main sections. First part covered the demographic details of the respondents’ including age, gender, educational level. The second part comprises 51 items which were adapted from previous researches as following; student background, five items, student experience, five items adapted from (Akaslan & Law, 2011 ), student collaborations, and, student interactions items adapted from (Bolliger & Inan, 2012 ), student autonomy, five items adapted from (Barnard et al., 2009 ; Pintrich, Smith, Garcia, & McKeachie, 1991 ), student satisfaction, six items adapted from (The blended learning impact evaluation at UCF is conducted by Research Initiative for Teaching Effectiveness, n.d. ). Moreover, effects of the students’ application, four items, students’ remembering, four items, students’ understanding, four items, students’ analyzing, four items, and students’ academic achievements, four items adapted from (Pekrun, Goetz, & Perry, 2005 ). The questionnaire has been distributed to the students after taking the online course.

Result and analysis

Cronbach’s Alpha reliability coefficient result was 0.917 among all research model factors. Thus, the discriminant validity (DV) assessment was carried out through utilizing three criteria, which are: index between variables, which is expected to be less than 0.80 (Bagozzi, Yi, & Nassen, 1988 ); each construct AVE value must be equal to or higher than 0.50; square of (AVE) between every construct should be higher, in value, than the inter construct correlations (IC) associated with the factor [49]. Furthermore, the crematory factor analysis (CFA) findings along with factor loading (FL) should therefore be 0.70 or above although the Cronbach’s Alpha (CA) results are confirmed to be ≥0.70 [50]. Researchers have also added that composite reliability (CR) is supposed to be ≥0.70.

Model analysis

Current research employed AMOS 23 to analyze the data. Both structural equation modeling (SEM) as well as confirmatory factor analysis (CFA) have been employed as the main analysis tools. Uni-dimensionality, reliability, convergent validity along with discriminant validity have been employed to assess the measurement model. (Bagozzi et al., 1988 ; Byrne, 2010 ; Kline, 2011 ) highlighted that goodness-of-fit guidelines, such as the normed chi-square, chi-square/degree of freedom, normed fit index (NFI), relative fit index (RFI), Tucker-Lewis coefficient (TLI) comparative fit index (CFI), incremental fit index (IFI), the parsimonious goodness of fit index (PGFI), thus, the root mean square error of approximation (RMSEA) besides the root mean-square residual (RMR). All these are tools which could be utilized as the assessment procedures for the model estimation. See Table  1 & Fig.  2 .

Measurement Model

Measurement model

Such type of validity is commonly employed to specify the size difference between a concept and its indicators and other concepts (Hair et al., 2012a , 2012b ). Through analysis in this context, discriminant validity has proven to be positive over all concepts given that values have been over 0.50 (cut-off value) from p  = 0.001 according to Fornell and Larcker ( 1981 ). In line with Hair et al. ( 2012a , 2012b ) . Bagozzi, Yi, & Nassen, (1998), the correlation between items at any two specified constructs must not exceed the square root of the average variance that is shared between them in a single construct. The outcomes values of composite reliability (CR) besides those of Cronbach’s Alpha (CA) remained about 0.70 and over, while the outcomes of the average variance extracted (AVE) remained about 0.50 and higher, indicating that all factor loadings (FL) were significant, thereby fulfilling conventions in the current assessment Bagozzi, Yi, & Nassen, (1998), and Byrne ( 2010 ). Following sections expand on the results of the measurement model. Findings of validity, reliability, average variance extracted (AVE), composite reliability (CR) as well as Cronbach’s Alpha (CA) have all been accepted, which also demonstrated determining the discriminant validity. It is determined that all the values of (CR) vary between 0.812 and 0.917, meaning they are above the cut-off value of 0.70. The (CA) result values also varied between 0.839 and 0.897 exceeding the cut-off value of 0.70. Thus, the (AVE) was similarly higher than 0.50, varying between 0.610 and 0.684. All these findings are positive, thus indicating significant (FLs) and they comply with the conventional assessment guidelines Bagozzi, Yi, & Nassen, (1998), along with Fornell and Larcker ( 1981 ). See Table  2 and Additional file  1 .

Structural model analysis

In the current study, the path modeling analysis has been utilized to examine the impact of students’ academic achievements among higher education institutions through the following factors (students’ background, students’ experience, students’ collaborations, students’ interaction, students’ autonomy, students’ remembering, students’ understanding, students’ analyzing, students’ application, students’ satisfaction), which is based on online learning. The findings are displayed then compared in hypothesis testing discussion. Subsequently, as the second stage, factor analysis (CFA) has being conducted on structural equation modeling (SEM) in order to assess the proposed hypotheses as demonstrated in Fig.  3 .

Findings for the Proposed Model Path analysis

As shown in both Figs.  3 and 4 , all hypotheses have been accepted. Moreover, Table  3 below shows that the fundamental statistics of the model was good, which indicates model validity along with the testing results of the hypotheses through demonstrating the values of unstandardized coefficients besides standard errors of the structural model.

Findings for the Proposed Model T.Values

The first direct five assumptions, students’ background, students’ experience, students’ collaborations, students’ interaction; students’ autonomy with students’ satisfaction, were addressed. In accordance with Fig.  4 and Table 3 , relations between students’ background and students’ satisfaction was (β = .281, t = 5.591, p  < 0.001), demonstrating that the first hypothesis (H1) has suggested a positive and significant relationship. Following hypothesis illustrated the relationship between students’ experience and students’ satisfaction (β = .111, t = 1.951, p  < 0.001), demonstrating that the second hypothesis (H2) proposed a positive and significant relationship. Third hypothesis illustrated the relationship between students’ collaborations and students’ satisfaction (β = .123, t = 2.584, p  < 0.001) demonstrating that the third hypothesis (H3) has suggested a positive and significant relationship. Additionally, the relationship between students’ background and students’ satisfaction was (β = .116, t = 2.212, p < 0.001), indicating that the fourth hypothesis (H4) has suggested a positive and significant relationship. Further to the above-mentioned findings, the relationship between students’ autonomy and students’ satisfaction was (β = .470, t = 7.711, p  < 0.001), demonstrating that the fifth hypothesis (H5) has suggested a positive and significant relationship. Moreover, in the second section, five assumptions were discussed, which are students’ satisfaction, students’ remembering, students’ understanding, students’ analyzing, students’ application along with students’ academic achievements.

As shown in Fig. 4 and Table 3 , the association between students’ satisfaction and students’ academic achievements was (β = .135, t = 3.473, p  < 0.001), demonstrating that the sixth hypothesis (H6) has suggested a positive and significant relationship. Following hypothesis indicated the relationship between students’ application and students’ academic achievements (β = .215, t = 6.361, p  < 0.001), indicating that the seventh hypothesis (H7) has suggested a positive and significant relationship. Thus, the eighth hypothesis indicated the relationship between students’ remembering and students’ academic achievements was (β = .154, t = 4.228, p  < 0.001), demonstrating that the eight hypothesis (H8) has suggested a positive and significant relationship. Additionally, the correlation between students’ understanding and students’ academic achievements was (β = .252, t = 6.513, p < 0.001), demonstrating that the ninth hypothesis (H9) has suggested a positive and significant relationship. Finally, the relationship between students’ analyzing and students’ academic achievements was (β = .179, t = 6.215, p < 0.001), demonstrating that the tenth hypothesis (H10) has suggested a positive and significant relationship. Accordingly, this current model demonstrated student’s compatibility to use online learning platforms to improve students’ academic achievements and satisfaction. This is in accordance with earlier investigations (Abuhassna & Yahaya, 2018 ; Al-Rahmi et al., 2018 ; Al-rahmi, Othman, & Yusuf, 2015c ; Barkand, 2017 ; Madjar et al., 2013 ; Salmon, 2014 ).

Discussion and implications

Developing a new hybrid technology acceptance model through combining TDT and BTT has been the major objective of the current research, which aimed to investigate the guiding factors towards utilizing online learning platforms to improve students’ academic achievements and satisfaction in higher education institutions. The current research is intensifying a step forward by implementing TDT along with a BTT model. Using the proposed model, the current research examined how students’ background, students’ experience, students’ collaborations, students’ interactions, and students’ autonomy positively affected students’ satisfaction. Moreover, effects of the students’ application, students’ remembering, students’ understanding, students’ analyzing, and students’ satisfaction positively affected students’ academic achievements. The current research found that students’ background, students’ experience, students’ collaborations, students’ interactions, and students’ autonomy were influenced by students’ satisfaction. Also, effects of the students’ application, students’ remembering, students’ understanding, students’ analyzing, and students’ satisfaction positively affected students’ academic achievements. This conclusion is consistent with earlier correlated literature. Thus, this reveals that learners first make sure whether using platforms of online learning were able to meet their study requirements, or that using platforms of online learning are relevant to their study process before considering employing such technology in their study. Learners have been noted to perceive that platforms of online learning is more useful only once they discover that such a technology is actually better than the traditional learning which does not include online learning platforms (Choy & Quek, 2016 ; Illinois Online Network, 2003 ). Using the proposed model, the current research examined how to improve students’ academic achievements and satisfaction. Thus, the following section will be a comparison between this study results and previous research, as follows.

The first hypotheses of this study demonstrated a positive and significant association between students’ prior background towards online platforms with their satisfaction. As clearly investigated in Osika and Sharp ( 2002 ) study, numerous learners deprived of these main skills enroll in the courses, struggle, and subsequently drop out. In addition, Bocchi, Eastman, and Swift ( 2004 ) investigation claimed that prior knowledge of students’ concerns, demands along with their anticipations is crucial in constructing an efficient instruction. Thus, to clarify, students must have prior knowledge and background before letting them into the online platforms. On the other hand, there are constant concerns about the online learning platforms quality in comparison to a face-to-face learning environment, as students do not have the essential skills required toward using online learning platforms (Illinois Online Network, 2003 ). Moreover, a study by Alalwan et al. ( 2019 ) discovered that Austrian learners still would rather choose face-to-face learning for communication purposes, and the preservation of interpersonal relations. This is due to the fact that learners do not as yet have the background knowledge and skills needed towards using online learning platforms. Additional research by Orton-Johnson ( 2009 ) among UK learners claimed that learners have not accepted online materials, and continue to prefer traditional context materials as the medium for their learning, which also indicates the importance of prior knowledge and background towards online platforms before going through such a technology.

The second hypotheses of this study proposed a positive and significant association between students’ experience along with students’ satisfaction, which revealed that putting the students in such an experience would provide and support them with the ability to overcome all difficulties that arise through the limits around the technical ability of the online platforms. This is in line with some earlier researches regarding the reasons that lead to people’s technology acceptance behavior. One reason is the notion of “conformity,” which means the degree to which an individual take into consideration that an innovation is consistent with their existing demands, experiences, values and practices (Chau & Hu, 2002 ; Moore & Benbasat, 1991 ; Rogers, 2003 ; Taylor & Todd, 1995 ). Moreover, (Anderson & Reed, 1998 ; Galvin, 2003 ; Lewis, 2004 ) claimed that most students who had prior experience with online education tended to exhibit positive attitudes toward online education, and it affects their attitudes toward online learning platforms.

The third hypotheses of this study demonstrated a positive and significant association among student collaboration with themselves in online platforms, which indicates the key role of collaboration between students in order to make the experiment more realistic and increase their ability to feel more involved and active. This is agreement with Al-rahmi, Othman, and Yusuf ( 2015f ) who claimed that type, quality, and amount of feedback that each student received was correlated to a student’s sense of success or course satisfaction. Moreover, Rabinovich ( 2009 ) found that all types of dialogue were important to transactional distance, which make it easier for the student to adapt to online learning platform. Also, online learning platforms enable learners to share then exchange information among their colleagues Abuhassna et al., 2020 ; Abuhassna & Yahaya, 2018 ).

Students’ interaction with the instructor in online platforms

The fourth hypothesis of this study proposed a positive and significant correlation between students’ collaborations and students’ satisfaction, which indicates the significance of the communication between students and their instructor throughout the online platforms experiment. These results agree with (Mathieson, 2012 ) results, which stated that the ability of communication between students and their instructor lowered the sense of separation between learner and educator. Moreover, in line with (Kassandrinou et al., 2014 ), communication guides learners to undergo constructive emotions, for example relief, satisfaction and excitement, which assist them to achieve their educational goals. In addition, (Furnborough, 2012 ) draws conclusion that learners’ feeling of cooperating with their fellow students effects their reaction concerning their collaboration with their peers. Moreover, Kassandrinou et al., 2014 focused on the instructor as crucial part as interaction and communication helpers, as they are thought to constantly foster, reassure and assist communication and interaction amongst students.

Student’s autonomy in online platforms

The fifth hypotheses of this study proposed a positive and significant relationship between student’s autonomy and online learning platforms, which indicates that students need a sense of dependence towards online platforms, which agrees with Madjar et al. ( 2013 ) who concluded that a learners’ autonomy-supportive environment provides these learners with adoption of more aims, leading to more learning achievements. Moreover, Stroet et al. ( 2013 ) found a clear positive correlation on the impacts of autonomy supportive teaching on motivation of learner. O’Donnell, Chang, and Miller ( 2013 ) also argues that autonomy is the ability of the learners to govern themselves, especially in the process of making decisions and setting their own course and taking responsibility for their own actions.

Student’s satisfaction in online platforms

The sixth hypotheses of this study proposed a positive and significant correlation between student’s satisfaction with online learning platforms, which indicates a level of acceptance by the students to adapt into online learning platforms. This is in agreement with Zhu ( 2012 ) who reported that student’s satisfaction in online platforms is a statement of confidence with the system. Moreover, Kirmizi ( 2014 ) study revealed that the predictors of the learners’ satisfaction were educator’s support, personal relevance and authentic learning, whereas the authentic learning is only the predictor of academic success. Furthermore, the findings of Bordelon ( 2013 ) stated and determined a positive correlation between both satisfaction and achievement. In addition, the results of Mahle ( 2011 ) clarified that student satisfaction occurs when it is realized that the accomplishment has met the learners’ expectations, which is then considered a short-term attitude toward the learning procedure.

Hypotheses seven, eight, nine and ten of this study proposed a positive and significant relationship between student’s academic achievements with online learning platforms, which indicates the key main role of online platform with students’ academic achievements. This agrees with Whitmer ( 2013 ) findings, which revealed that the associations between student usage of the LMS and academic achievement exposed a highly systematic relationship. In contrast, Barkand ( 2017 ) found that there is no significant difference in students’ academic achievements in utilizing online platforms regarding students’ academic achievements, which is due to the fact that academic achievement towards online learning platforms requires a certain set of skills and knowledge as mentioned in the above sections in order to make such technology a success.

The seventh hypotheses of this study proposed a positive and significant correlation between students’ application and students’ academic achievements, which indicates the major key of applying in the learning process as an effected element. This is in line with the Computer Science Teachers’ Association (CSTA) taskforce in the U. S (Computer Science Teachers’ Association (CSTA), 2011 ), where they mentioned that applying elements of computer skills is essential in all state curricula, directing to their value for improving pupils’ higher order thinking in addition to general problem-solving abilities. Moreover, Gouws, Bradshaw, and Wentworth ( 2013 ) created a theoretical framework which drawn education computational thoughts compared to cognitive levels established from Bloom’s Taxonomy of Learning Purposes. Four thinking skill levels have been utilized to assess the ‘cognitive demands’ initiated by computational concepts for instance abstraction, modelling, developing algorithms, generating automated processes. Through the iPad app, LightBot. thinking skills remained recognizing (which means recognize and recall expertise correlating to the problem); Understanding (interpret, compare besides explain the problem); whereas, applying (make use of computer skills to create a solution) then Assimilating (critically decompose and analyses the problem).

The eighth hypotheses of this study proposed a positive and significant correlation between students’ remembering and students’ academic achievements, which indicates the importance of remembering as a process of retrieving information relating to what needed to be done and/or outcome attributes) over the procedure of learning according to Bloom’s Taxonomy of Educational Objectives. Additionally, Falloon ( 2016 ) claimed that responding to data indicated the use of general thinking skills to clarify and understand steps and stages needed to complete a task (average 29%); recalling or remembering information about a task or available tools (average 13%); and discussing and understanding success criteria (average 3%).

The ninth hypotheses of this study proposed a positive and significant correlation between students’ understanding and students’ academic achievements, which indicates its significance with the academic achievements as a process of criticizing the task or the problem faced by the students into phases or activities to help understanding of how to resolve the problem. The current results agree with Falloon ( 2016 ) who demonstrated the necessity to build understanding over the thinking processes employed by students once they are engaged in their work. In addition, Falloon ( 2016 ) suggested that the purpose and nature of questioning was broader than this, with questioning of self and others being an important strategy in solution development. In many respects, the questioning for those students was not much a perspective, although more a practice, to the degree that assisted them to understand their tasks, analyze intended or developed explanations and to evaluate their outcomes.

The tenth hypotheses of this study proposed a positive and significant correlation between students’ understanding and students’ academic achievements, which reveals the importance of analysis as a process of employing general thinking besides computational knowledge in order to realize the challenges through using online platforms, in addition to predictive thinking to categorize, explore and fix any possible errors throughout the whole process. Falloon ( 2016 ) claimed that analyzing was often a collaborative procedure between pairs receiving and giving counseling from others to assist in solving complications. On the other hand, online learning platforms are highly dependent on connecting and sharing as a basic strategy that needs to be employed over all stages of online learning settings, whether between students and students, or between students and their instructor. Moreover, Falloon ( 2016 ) findings showed that Analyzing (average 17%) was present in various phases of these online students’ work, which is based on what phase they were at together with their tasks, despite the fact that most analysis was associated with students depending on themselves during online process.

Conclusion and future work

In this investigation, both transactional distance theory (TDT) and Bloom’s Taxonomy theory (BTT) have been validated in the educational context, providing further understanding towards the students’ prospective perceptions on using online learning platforms to improve students’ academic achievement and satisfaction. The contribution that the current research might have to the field of online learning platforms have been discussed and explained. Additional insights towards students’ satisfactions and students’ academic achievements have also been presented. The current research emphasizes that the incorporation of both TDT and BTT can positively influence the research outcome. The current research has determined that numerous stakeholders, for instance developers, system designers, along with institutional users of online learning platforms reasonably consider student demands and needs, then ensure that the such a system is effectively meeting their requirements and needs. Adoption among users of online learning platforms could be broadly clarified by the eleven factor features which is based on this research model. Thus, the current research suggests more investigation be carried out to examine relationships among the complexity of online learning platforms combined with technology acceptance model (TAM).

Recommendations for stakeholders of online platforms

Based on the study findings, the first recommendation would be for administrators of higher institution. In order to implement online learning, there must be more interest given to the course structure design, whereas it should be based on theories and prior literature. Moreover, instructor and course developer need to be trained and skilled to achieve online learning platforms goals. Workshops and training sessions must be given for both instructors and students to make them more familiar in order to take the most advantages of the learning management system like Moodle and LMS. The software itself is not enough for creating an online learning environment that is suitable for students and instructors. If instructors were not trained and unaware of utilizing the software (e.g. Moodle) in the class, then the quality of education imparted to students will be jeopardized. Training and assessing the class instructor and making modifications to the software could result in a good environment for the instructor and a quality education for the student. Both students’ satisfaction and academic achievements depends on their prior knowledge and experience in relation to online learning. This current research intended to investigate student satisfaction and academic achievements in relation to online learning platforms in on of the higher education in Malaysia. Future research could integrate more in relation to blended learning settings.

Availability of data and materials

All the hardcopy questionnaires, data and statistical analysis are available.

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The study involved both undergraduate and graduate students at unviersiti teknologi Malaysia (UTM), an ethical approve was taken before collecting any data from the participants

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Hassan Abuhassna, Waleed Mugahed Al-Rahmi, Noraffandy Yahya, Megat Aman Zahiri Megat Zakaria & Azlina Bt. Mohd Kosnin

Faculty of Engineering, School of Civil Engineering, Universiti Teknologi Malaysia, UTM, 81310, Skudai, Johor, Malaysia

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Abuhassna, H., Al-Rahmi, W.M., Yahya, N. et al. Development of a new model on utilizing online learning platforms to improve students’ academic achievements and satisfaction. Int J Educ Technol High Educ 17 , 38 (2020). https://doi.org/10.1186/s41239-020-00216-z

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• Online learning platforms
• Students’ achievements
• student’s satisfaction
• Transactional distance theory (TDT)
• Bloom’s taxonomy theory (BTT)

A systematic review of collaborative digital platforms: structuring the domain and research agenda

• Original Paper
• Published: 02 September 2023

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• Douglas Wegner   ORCID: orcid.org/0000-0001-8634-5971 1 ,
• Alexandre Borba da Silveira   ORCID: orcid.org/0000-0002-5620-2125 2 ,
• Diego Marconatto   ORCID: orcid.org/0000-0002-9458-9199 1 &
• Maciej Mitrega   ORCID: orcid.org/0000-0003-4043-5589 3  

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A Correction to this article was published on 09 October 2023

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This article reviews the emerging literature on collaborative digital platforms, a stream of research focused on platforms that spur the collaboration of participants of various kinds and aim at some benefits beyond financial gains. Based on a systematic literature review, we organize and synthesize the literature on the topic in three sub-streams of research: digital platforms that spur collaboration, platform cooperativism, and open cooperatives. The first sub-stream refers to digital platforms aiming to stimulate participants’  collaboration. These platforms may be owned by a central actor or sponsored by public agencies. The second sub-stream of research conforms to digital platforms organized around cooperative principles, i.e., participants are the platform owners and governors. The third sub-stream—open cooperatives—refers to a novel and radical form of digital cooperation based on open source and open data, where members collaborate without strong regulations. The literature analysis allows us to structure the domain and push the boundaries of the field by identifying several avenues for future studies, which are organized  concerning theory, methods and context, as well as some specific topics that demand special attention.

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The Role of Actors in Platform Ecosystems: A Systematic Literature Review and Comparison Across Platform Types

Management Problems in Cooperative Platforms and Traditional Cooperatives

Platforms: a multiplicity of research opportunities

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08 october 2023.

In the original version of this article, the fourth author’s affiliation was published incorrectly as “University of Economics in Katowice, Katowice, Poland” instead of “Department of Marketing and Business, Faculty of Economics, VSB – Technical University of Ostrava, Ostrava, Czech Republic” and has now been corrected.

09 October 2023

A Correction to this paper has been published: https://doi.org/10.1007/s11846-023-00701-5

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This study was partially financed by the Foundation for Research Support of the State of Rio Grande do Sul, Brazil (FAPERGS - Grant 22/2551-0000505-5), National Council for Scientific and Technological Development, Brazil (CNPq - Grant 308011/2020-1) and partially supported within the project SGS “Analysis of Consumer Attitudes on B2C Market”—Project Registration Number SP2022/126.

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Wegner, D., da Silveira, A.B., Marconatto, D. et al. A systematic review of collaborative digital platforms: structuring the domain and research agenda. Rev Manag Sci (2023). https://doi.org/10.1007/s11846-023-00695-0

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How to Identify the Best Platforms for Publishing

When trying to identify the best platforms for publishing your research, you have to consider several factors, including:

• Does the platform support your research journey ? Can you collaborate with other authors and researchers, discover public groups and research papers and manuscripts (including Open Access work), view interactive graphs, images, tables, etc., track citations, and build a professional research profile?
• Is the platform easy to use ? Does it offer rich functionalities that are easy to understand, and if so, which ones?
• Does it use artificial intelligence and machine learning ? Automated actions (email alerts, etc.) can help you unlock breakthroughs faster and deliver deeper insights.
• What security and governance does it have ? Platforms must be secure and compliant according to local regulations since researchers often deal with sensitive data.

The 5 Best Platforms to Publish Academic Research

Researchgate.

ResearchGate is a platform hosting over 135 million publication pages with a community of 20 million scientists. The platform allows you to show off your work, access papers and advice from other researchers, make contacts and even find jobs. Some of its more prominent features include:

• Dedicated Q&A section with searchable keywords to target experts in your particular field or area of study
• Ability to create a personal profile page where you can display all research-specific details about yourself, including up to five pieces of work (including datasets and conference papers)
• In-depth stats on who reads your work and the ability to track your citations
• A private messaging service that allows you to send messages to other researchers
• A comments section to provide feedback when viewing a paper
• A “projects” section to tell others about your upcoming work.

In addition, it's completely free to use!

Academia is a research-sharing platform with over 178 million users, 29 million papers uploaded, and 87 million visitors per month. Their goal is to accelerate research in all fields, ensure that all research is available for free and that the sharing of knowledge is available in multiple formats (videos, datasets, code, short-form content, etc.). Some of their more prominent features include:

• Mentions and search alerts that notify you when another researcher cites, thanks, or acknowledges your work, and automatic reports of search queries
• Ability to create a personal profile page
• “Profile visitor” and “readers” features let you know the title and location of those who visit your profile or read your papers so you can learn about their research interests and get in touch
• A “grants” feature to allow you to find new grants and fellowships in your field
• Advanced research discovery tools allow you to see full texts and citations of millions of papers.

The platform is based on a “freemium” business model, which provides free access to research for everyone, and paid capabilities to subscribers.

ScienceOpen

ScienceOpen is a discovery platform that empowers researchers to make an impact in their communities. The platform is committed to Open Science, combining decades of experience in traditional publishing, computing, and academic research to provide free access to knowledge to drive creativity, innovation, and development. Some of their more prominent features include:

• You can publish your most recent paper as a preprint that’s citable and includes a DOI to share with peers immediately and enhance visibility
• A multidimensional search feature for articles with 18 filters and the ability to sort results by Altmetric scores , citations, date, and rating
• Ability to create a personal profile with minimal upkeep necessary
• Access to a suite of metrics (usage, citations, etc.) of your publications
• Ability to follow other researchers to stay up-to-date on their work and expand your network.

The platform is free to use, although some features (like publishing your preprint) may cost money.

IOPscience is a platform that embraces innovative technologies to make it easier for researchers to discover and access technical, scientific, and medical content while managing their own research content. They participate in several programs that offer researchers in developing countries several ways to gain access to journals at little or no cost. Some of their other features include:

• An enhanced search filtering feature allows you to find relevant research faster
• A social bookmarking feature allows you to interact with other researchers and share articles
• Ability to create a personal profile, customize your alerts, view recently published articles within your field or area of interest, and save relevant papers or articles
• Ability to receive email alerts and RSS feeds once new content is published.

IOPscience is free to use and functions on an Open Access policy, which you can check here .

Orvium is an open, community-based research platform that allows researchers, reviewers, and publishers to share, publish, review, and manage their research. Orvium protects your work with built-in blockchain integration to ensure that you maintain the copyright of your work and not only. Some of our more notable features include:

• Access to a modern web platform with Google indexing, notifications, and mobile-ready features
• Ability to manage your entire publication process, with control over when you submit, receive peer reviews, and publish your paper
• “Collaboration” and “full traceability” features allow you to track your profile impact, get in touch with other researchers, and have ownership over your work
• Recognition badges or economic rewards are given when you peer-review.

Orvium is completely free to use.

Orvium Makes Choosing a Platform Easy

No matter what platform or community you choose to be a part of, you now know what you need to look for when choosing one. You also learned about five excellent platforms where you can publish your academic research. Orvium will remain your one-stop-shop platform for all your research needs. Do you want to know how Orvium and our communities work? Check out our platform or contact us with any questions you may have.

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• Best Stock Brokers Best Stock Trading Apps Best Stock Trading Platforms for Beginners Best Paper Trading Platforms Best Day Trading Platforms
• Best Futures Trading Platforms Best Options Trading Platforms Best Penny Stock Brokers Best International Brokers All Guides arrow_right_alt

• Robinhood vs Webull Charles Schwab vs E*TRADE Fidelity vs Robinhood TradeStation vs Interactive Brokers
• E*TRADE vs Interactive Brokers Charles Schwab vs Fidelity Merrill Edge vs Fidelity Compare Brokers arrow_right_alt
• done About Us done How We Test done Why Trust Us done Our Policy on AI done Media
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Best Stock Trading Platforms for Beginners of May 2024

Written by Sam Levine, CFA, CMT Edited by Carolyn Kimball Fact-checked by Steven Hatzakis Reviewed by Blain Reinkensmeyer

Are you ready to start investing, but aren’t quite sure where to begin? It’s easier than ever to get started with your first broker account. Brokers continue to roll out or enhance beginner-friendly features such as fractional shares, practice accounts (also called paper trading or simulated trading), and basic investor education.

I opened accounts and entered trades at 17 online brokers and chose the top five that I recommend the most for beginners. All the online brokers on this list are easy to use and offer great investor education. But, after spending a year testing, I’ve found they each have unique strengths that will appeal to different investors. Here are my faves for 2024 and why they made it onto my shortlist.

Why you can trust StockBrokers.com

Since 2009, we've helped over 20 million visitors research, compare, and choose an online broker. Our writers have collectively placed thousands of trades over their careers. Here's how we test .

Best Trading Platforms for Beginners

• Fidelity - Best overall for beginners
• Merrill Edge - Best research for beginners
• E*TRADE - Best trading app for beginners
• Charles Schwab - Outstanding market research
• Interactive Brokers - Best for global investors
• Robinhood - Best for Ease of Use
• Ally Invest - Best for Ally Bank customers

Best overall for beginners

Fidelity is a winner for beginners, thanks to its plethora of educational resources that includes a Learning Center stocked with videos, infographics, and even podcasts. Fidelity also offers an innovative Youth Account – a first-of-its-kind brokerage account for teens aged 13 to 17. Read full review

• Excellent research and mobile app
• Top-notch education
• Decades of reliable client service
• No dedicated mobile app for active trading

Best research for beginners

Merrill Edge offers $0 trades with industry-leading research tools and customer rewards. Learning about investing is a pleasant experience, thanks to excellent organization, quality and in-house curated content. Its Stock Stories and Fund Stories do phenomenal jobs presenting information in a friendly way. Read full review

• Portfolio Story, Dynamic Insights, and the Stock and Fund Stories are groundbreaking features
• High-quality proprietary research
• Some site elements slow to load
• No crypto, futures, forex or penny stocks

Best trading app for beginners

Earning a recommendation based on its trading platform alone, E*TRADE is great for any beginner stock trader. Power E*TRADE is easy to use and offers features including paper (practice) trading and note-taking. Its educational content, though plentiful, can be a challenge to navigate. Read full review

• Watch lists are the best in the business
• Smooth mobile navigation
• High-quality high-net-worth Morgan Stanley proprietary research
• Cryptocurrencies not currently available
• Margin rates are high compared to other brokers

Outstanding market research

Charles Schwab is a terrific all-around choice for everyday investors that offers a thorough educational experience and support for beginners, with its Choiceology podcast a standout. Paper (practice) trading is not available, however. Read full review

• TD Ameritrade’s excellent thinkorswim trading platforms now available
• Trading-friendly app and browser enhancements
• Exceptional high net worth services
• No cryptocurrency trading
• Mutual fund fees are complex

Best for global investors

Beginners and foreign stock aficionados will enjoy using Global Trader, which allows fractional stock trades, options trading and convenient access to foreign shares. Everything is clearly laid out and easy to operate. I’d rank Global Trader above many apps from beginner-focused brokers. Read full review

• Astounding array of customizable tools
• Allows trading in foreign markets
• Convenient apps for individual investors
• Restrictive trading permissions
• Main platforms might feel cold

Best for Ease of Use

Robinhood is very easy to use and its educational content is a joy to read. But, in today’s competitive market for your investing dollars, there are several more compelling options among brokerages. Read full review

• Famously easy to use
• Extensive crypto support with zero commissions and no markups or markdowns
• Learn section has some excellent writing
• Charges $5 monthly fee for data and research that’s free at many other brokers
• Limited investment choices
• Not enough tools for active trading

Best for Ally Bank customers

For current Ally customers looking to invest in stocks, Ally's universal-accounts experience and easy-to-use website offer a convenient solution. Its website is far stronger than the mobile app. Read full review

• Excellent banking via Ally Bank
• Universal account management
• $0 stock and ETF trades alongside a $0 minimum deposit
• Trails industry leaders in areas including platforms, tools, research, and education

compare_arrows Compare trading platforms head-to-head

Use the broker comparison tool to compare over 150 different account features and fees.

Compare brokers now

Winners Summary

Best overall for beginners - fidelity.

Fidelity, our 2024 winner for Best Overall Broker, is also my top pick for beginners. Our testing found that Fidelity has two beginner-friendly mobile apps, an extensive investor education library and high-quality independent research. Fidelity is easy to use and allows fractional trades of stock and ETF shares. It also offers a Youth account, which netted our Best Innovation award in 2022. Read review .

Screenshot tour of Fidelity's market research

Screenshot tour of Fidelity's educational resources

Best research for beginners - Merrill Edge

If you’re interested in investing in individual stocks or funds, my testing found that Merrill Edge’s Stock Stories and Fund Stories do phenomenal jobs presenting highly relevant info in a friendly way. I don’t think there’s a better way for everyday investors to learn how to research stocks than starting with Merrill’s Stock or Fund Stories. If I had these available when I taught stock investing at a business school, I would have used them in my class.

Once you’ve mastered the Stories and are ready to take a deeper dive into the numbers, Merrill has an extensive selection of Bank of America Securities and third-party research at the ready. Read review .

Screenshot tour of Merrill Edge's market research

Screenshot tour of Merrill Edge's educational resources

The Stock Story feature on the Merrill Edge mobile app.

Best trading app for beginners - E*TRADE

Our testing found that E*TRADE has the best trading app for beginners and has one of the best trading websites, too. New investors will appreciate the intuitive layouts and well-organized menus of portfolio and market information.

The same can’t be said for its investor education, which we found to be a mixed bag in our testing. E*TRADE does not allow clients to buy fractional shares, but does offer paper trading on its advanced trading platform, Power E*TRADE, at no cost. Read review .

Screenshot tour of E*TRADE's market research

Screenshot tour of E*TRADE's educational resources

Pricing and fees comparison

Here's a comparison of pricing across beginner trading platforms. To compare all our collected data side by side, check out our online broker comparison tool .

Beginner education comparison

Here's a comparison of the most popular educational features offered by beginner trading platforms. To compare all our collected data side by side, check out our online broker comparison tool .

Trading platforms tested, data findings

As part of our research process, we create a list of features, set strict definitions for each so our testing is uniform, collect the data, then extrapolate the resulting data to see how common each feature is across the industry as a whole. Here's our findings. To compare all our collected data side by side, check out our online broker comparison tool .

What is a trading platform and how does it work?

A trading platform, otherwise known as an online brokerage account, allows you to buy and sell investments via computer or mobile app. The brokerage holds your investmenta and deposited cash for you and provides activity reports and account statements. It also credits any interest accrued and dividends to your account. To open an online broker account in the United States, you will need a Social Security number and you will be required to enter basic financial information such as your name, address, phone number, and trading experience.

In the United States, brokers are regulated by both FINRA and the SIPC. The SIPC insures $500,000 per account including up to $250,000 in cash against theft or the firm going belly-up. It’s important to remember, however, that insurance does not protect any investor against losses due to market fluctuations.

How much money do you need to start investing?

Most trading platforms in the United States don’t require minimum account balances. Now you can open an account, fund it with a dollar or two, and buy a fraction of a share of stock. You can also practice investing with no money at all at a broker that offers virtual trading, also called paper trading. See our top broker picks for paper trading .

Which type of trading is best for beginners?

Beginners should consider starting off with swing trading, which means holding an investment for more than one day and less than a couple of months. It’s less time-consuming and stressful than day trading. Stocks are particularly good for beginners to test the waters. Wait until you have more experience before using options, short selling, and buying on margin.

Learn more: Are penny stocks worth investing in? Read our guide, Best Brokers for Penny Stock Trading . Not sure if trading is right for you? Check out our guide on how to invest on our sister site, investor.com.

Can I teach myself how to trade?

Yes, you can teach yourself to trade, provided you have realistic expectations and stay at it through a full market boom-and-bust cycle. Don’t invest more than a fraction of your trading capital at once, and keep a trading journal noting why you entered and exited each trade and how well that trade performed. Most traders fail because they focus on chasing the upside more than managing risk. Dive deeper: Learn more about trading journals for stock trading on our sister site, investor.com.

How do beginners trade stocks?

• Open a self-directed brokerage account.
• Decide how much money you can afford to risk.
• Deposit or transfer the money to your new account.
• Learn how to place an order and view your stocks at the broker you’ve chosen.
• Practice trading, either through a virtual portfolio or very small amounts of your own money with each trade.
• Keep a trading journal.
• Read, read, read. Here's a list of the best stock trading books on our sister site, investor.com.
• Follow market news and practice forming your own opinions.
• Monitor your results closely and adjust when it’s not working. Stay with it.

What is paper trading?

Paper trading, or virtual trading, is a trading platform feature that enables the trading of stocks, ETFs, and options with virtual currency (fake money). This helpful learning tool is popular with beginners and is a great way to practice stock trading without risking real money. Dive deeper: Best brokers for paper trading .

What are fractional shares?

A fractional share is a portion of a full share of a publicly traded company. Fractional shares enable investors with smaller budgets to buy a stake in companies with high stock prices. For example, instead of spending over $87 to buy one Amazon (AMZN) share, a trader could purchase a $10 fractional share – and then own a proportional fraction of that share. A real-world example is Charles Schwab's Schwab Stock Slices, which are fractional shares of any company in the S&P 500 and carry a minimum purchase of $5. Other brokers that offer fractional trading include Fidelity, Interactive Brokers, Webull, SoFi Invest, and Robinhood.

What is a market order?

A market order is an order to buy or sell a security (such as stock) at the current best-available market price. Market orders are the most common type of order, as they are the fastest and easiest way to buy and sell shares.

What is a limit order?

A limit order lets you buy or sell a security at a pre-specified price or better. Since limit orders are fixed to a prespecified price, they will only fill when the limit price is reached. Limit orders are best when you know the exact price you want to buy or sell a stock.

Is online trading safe?

Online trading is safe if you use a regulated online stock broker and never invest more than you are willing to lose. Trading stocks online is inherently risky. Start with a small amount of money, read investing books, and keep it simple by buying and holding for the long term rather than trying to time the market.

Our Research

Why you should trust us.

Sam Levine, CFA, CMT , the lead writer for StockBrokers.com, has over 30 years of investing experience and actively trades stocks, ETFs, options, futures, and options on futures. He's held roles as a portfolio manager, financial consultant, investment strategist and journalist. He holds the Chartered Financial Analyst (CFA) and the Chartered Market Technician (CMT) designations and served on the board of directors of the CMT Association.

Blain Reinkensmeyer , head of research at StockBrokers.com, has been investing and trading for over 25 years. After having placed over 2,000 trades in his late teens and early 20s, he became one of the first in digital media to review online brokerages. Blain created the original scoring rubric for StockBrokers.com and oversees all testing and rating methodologies.

For this guide:

• Whenever possible, we used our own brokerage accounts for testing. For several brokers, we used a test account that was provided to us.
• We collected more than 3,000 data points (196 per broker).
• We tested each online broker's website, browser-based trading platform (where applicable), downloadable desktop trading platform (where applicable), and of course, the mobile app (or apps in the case of several brokers).

How we tested

Our research team rigorously tests the most important features sought by beginning investors and traders, including the quality and variety of educational resources, ease of use of any available trading platforms and the availability of of market research and commentary suitable for novices. We also looked at which brokers offer unique features like webinars, live seminars, videos, progress tracking, paper trading (aka a stock market simulator) and interactive educational elements such as quizzes.

StockBrokers.com uses a variety of computing devices to evaluate trading platforms. Our reviews were conducted using the following devices: iPhone 12 Pro, iPhone 15 Pro Max, MacBook Pro M1 with 8 GB RAM running the current MacOS, and a Dell Vostro 5402 laptop i5 with 8 GB RAM running Windows 11 Pro. In testing platforms and apps, our reviewers place actual trades for a variety of instruments.

As part of our data check process, we sent a data profile link to each broker summarizing the data we had on file and the data they provided us last year, with a field for entering any data that had since changed. For the brokers that filled out these profiles, we audited the information for any discrepancies between our data and the broker’s data to ensure accuracy.

As part of our review process, all brokers had the opportunity to provide updates and key milestones in a live meeting that took place in the fall. Meetings with broker teams also took place throughout the year as new products rolled out. Insights gathered from these calls helped steer our testing efforts to ensure every feature and tool was assessed.

Trading platforms tested

• Ally Invest review
• Charles Schwab review
• eToro review
• E*TRADE review
• Fidelity review
• Firstrade review
• Interactive Brokers review
• J.P. Morgan Self-Directed Investing review
• Merrill Edge review
• Public.com review
• Robinhood review
• SoFi Invest review
• tastytrade review
• TradeStation review
• Tradier review
• Vanguard review
• Webull review

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Popular stock broker reviews, about the editorial team.

Sam Levine has over 30 years of experience in the investing field as a portfolio manager, financial consultant, investment strategist and writer. He also taught investing as an adjunct professor of finance at Wayne State University. Sam holds the Chartered Financial Analyst and the Chartered Market Technician designations and is pursuing a master's in personal financial planning at the College for Financial Planning. Previously, he was a contributing editor at BetterInvesting Magazine and a contributor to The Penny Hoarder and other media outlets.

Carolyn Kimball is managing editor for Reink Media and the lead editor for the StockBrokers.com Annual Review. Carolyn has more than 20 years of writing and editing experience at major media outlets including NerdWallet, the Los Angeles Times and the San Jose Mercury News. She specializes in coverage of personal financial products and services, wielding her editing skills to clarify complex (some might say befuddling) topics to help consumers make informed decisions about their money.

Steven Hatzakis is the Global Director of Research for ForexBrokers.com. Steven previously served as an Editor for Finance Magnates, where he authored over 1,000 published articles about the online finance industry. Steven is an active fintech and crypto industry researcher and advises blockchain companies at the board level. Over the past 20 years, Steven has held numerous positions within the international forex markets, from writing to consulting to serving as a registered commodity futures representative.

Blain Reinkensmeyer has 20 years of trading experience with over 2,500 trades placed during that time. He heads research for all U.S.-based brokerages on StockBrokers.com and is respected by executives as the leading expert covering the online broker industry. Blain’s insights have been featured in the New York Times, Wall Street Journal, Forbes, and the Chicago Tribune, among other media outlets.