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E-learning programs exist in a wide variety of formats. Without a framework for distinguishing between different e-learning programs, it is a challenge for researchers to compare their effectiveness or identify characteristics of e-learning that contribute to learning effectiveness. Based on general theories of learning, we develop a typology that compares e-learning programs in terms of the nature of the learning interactions they provide for learners in three dimensions: degree of interaction, learner control of interactions, and informational value of interactions. The typology dimensions apply to learner–instructor, learner–learner, and learner–instructional material interactions. We also discuss important theoretical implications of the typology. First, we show the utility of the typology for comparing the effectiveness of different e-learning programs. Second, we apply the typology dimensions to develop a theoretical framework for e-learning research that provides a foundation for examining factors that influence learning effectiveness in an e-learning program. The framework identifies e-learning program characteristics, learner characteristics, and contextual factors that impact learning effectiveness in different e-learning environments. It also shows how the typology dimensions align with learning goals to influence learning effectiveness.

This study intends to examine whether the reasons learners like or dislike a learning environment can help explain the differences in the characteristics of the learner and whether learner characteristics can influence a learner's preference for a learning environment.

This study adopted an exploratory sequential mixed methods research design. The study first conducted focus groups with university students to uncover their learner characteristics by examining the reasons they liked or disliked a learning environment. This was followed by a questionnaire survey to explore how these learner characteristics influenced learner inclination for a learning environment. The survey data were analysed using exploratory and confirmatory factor analysis (partial least squares structural equation modelling).

The findings showed that two types of learner characteristics, i.e. online learner characteristics and classroom learner characteristics, significantly affected learner inclination for a learning environment. Analyses also indicated that learner demographics had no major moderating effect between learner characteristics and learner inclination for a learning environment.

The findings can be useful for education institutions, learning designers and academics to design engaging learning activities to better support different learning needs.

This study makes a novel attempt to distinguish learner characteristics based on the reasons learners like or dislike a learning environment and establishes that individual learners' characteristics play a role in influencing their preference for a specific learning environment.

E‐learning is an organizationally risky investment given the cost and poor levels of adoption by users. In order to gain a better understanding of this problem, the aim of this paper is to conduct a study into the use of e‐learning in a rail organization.

Using an online survey, employees of a rail‐sector organization were questioned about their use and likelihood of adoption of e‐learning. This study explores the factors that affect the way in which learners experience and perceive such systems. Using statistical analysis, 12 hypotheses are tested and explored. Relationships between learning satisfaction, intention to adopt, and the characteristics of e‐learning systems were established.

The study found that e‐learning characteristics can buffer the relationship between learner characteristics and intention to adopt further e‐learning in the future. Further, the paper found that high levels of support can compensate individuals who are low in technological efficacy to adopt e‐learning.

The cross‐sectional design of the study and its focus on measuring intention to adopt as opposed to actual adoption are both limitations. Future research using longitudinal design and research employing a time lag design measuring actual adoption as well as intention are recommended.

From a practical perspective, organizations can focus on the actual content and authenticity of the learning experience delivered by the e‐learning system to significantly impact how employees will perceive and use e‐learning in the future. Low technological efficacy individuals tend not to adopt new technology. Instead of changing individuals’ personalities, organizations can implement supportive policies and practices which would lead to higher e‐learning adoption rate among low efficacy individuals.

The study integrates technology adoption and learning literatures in developing enablers for e‐learning in organizations. Further, this study collects data from rail employees, and therefore the findings are practical to an industry.

This study aims to highlight the role of individual and instructional design factors on training transfer and explore and review focal training factors that influence the transfer of training.

A conceptual framework has been developed for this study to better present the prevalent factors that have been empirically validated.

The facets of transfer encompassed in this study have gathered enough evidence to demonstrate a positive association with the transfer. This review points out that all predictor variables (learner characteristics, instructional design and environmental factors) positively and significantly influence the transfer of training. A mix of variables is proposed, with further investigation recommended to develop a robust training transfer hypotheses.

This study contributes to the training transfer literature in three ways. First, the authors have shown the positive influence of pre-training factors on training transfer. This study is grounded in a robust theoretical framework, thus fulfilling the previous gaps. This conceptual study elaborates on the importance of factors to establish the training transfer hypothesis. Future studies should test the suggested framework and look for other things that could help learners be more effective and motivated so that training can be more effective and transferable.

This study has significant findings for organizations operating in the current business scenario to enhance learning transfer, where the transfer is very low and a significant cause of concern for every organization. By making management aware of the success factors of training transfer, they can ensure a better training transfer. In this study, the transfer design considerations are discussed in detail, and their practical consequences are provided for training transfer in general and training professionals in particular.

The training transfer literature showcases two significant gaps; first, it lacks appropriate characteristics, and second, there is variability in the results. This study bridges the gaps and attempts to advance our understanding of training transfer grounded in the theoretical framework by focusing on learner characteristics, instructional design and work environment in training transfer, which predictor variables can improve training transfer. This study also contributes to the current training transfer literature by proposing a combination of factors that provide a theoretical foundation for developing a solid training transfer theory.

The purpose of this paper is to identify the different sequence generation techniques for learning, which are applied to a broad category of personalized learning experiences. The papers have been classified using different attributes, such as the techniques used for sequence generation, attributes used for sequence generation; whether the learner is profiled automatically or manually; and whether the path generated is dynamic or static.

The search for terms learning sequence generation and E-learning produced thousands of results. The results were filtered, and a few questions were answered before including them in the review. Papers published only after 2005 were included in the review.

The findings of the paper were: most of the systems generated non-adaptive paths. Systems asked the learners to manually enter their attributes. The systems used one or a maximum of two learner attributes for path generation.

The review pointed out the importance and benefits of learning sequence generation systems. The problems in existing systems and future areas of research were identified which will help future researchers to pursue research in this area.

The purpose of this study is to analyze from multiple perspectives, so as to form an effective massive open online course (MOOC)personalized recommendation method to help learners efficiently obtain MOOC resources.

This study introduced ontology construction technology and a new semantic association algorithm to form a new MOOC resource personalized recommendation idea. On the one hand, by constructing a learner model and a MOOC resource ontology model, based on the learner’s characteristics, the learner’s MOOC resource learning preference is predicted, and a recommendation list is formed. On the other hand, the semantic association algorithm is used to calculate the correlation between the MOOC resources to be recommended and the learners’ rated resources and predict the learner’s learning preferences to form a recommendation list. Finally, the two recommendation lists were comprehensively analyzed to form the final MOOC resource personalized recommendation list.

The semantic association algorithm based on hierarchical correlation analysis and attribute correlation analysis introduced in this study can effectively analyze the semantic similarity between MOOC resources. The hybrid recommendation method that introduces ontology construction technology and performs semantic association analysis can effectively realize the personalized recommendation of MOOC resources.

This study has formed an effective method for personalized recommendation of MOOC resources, solved the problems existing in the personalized recommendation that is, the recommendation relies on the learner’s rating of the resource, the recommendation is specialized, and the knowledge structure of the recommended resource is static, and provides a new idea for connecting MOOC learners and resources.

This paper aims to give a critical review of the empirical information seeking and retrieval (IS&R) literature focusing on learning related information behavior. It also aims to propose the task‐based approach to link research on learning and information behavior in learning tasks and to organize and interpret the literature.

This paper takes the form of a critical literature review.

Researchers have identified patterns of information behavior and variations in the conceptions of seeking and using information for learning tasks. The results are fragmented and a framework should be developed to guide further research. Learners' information behavior in learning tasks may be attributable to various explanatory factors, which have not been taken into account in any single study. The concept of focus formulation appears to link information seeking and learning. The role of the writing process in learners' information behavior and the characteristics of the resulting documents have received least attention in the research field.

The review emphasizes task‐based IS&R literature. Learning research may shed more light on specific questions.

The paper may help teachers in designing learning tasks.

The paper provides a synthesis of recent studies on information behavior in learning tasks and identifies new paths for further research.

The purpose of this study is to identify the learner characteristics attributable to the likelihood and the duration of programme completion in the Bachelor of Science (BSc) and Bachelor of Technology Honours in Engineering (BTech) degree programmes of the Open University of Sri Lanka (OUSL).

Data were gathered from the re-registrants for the degree programmes in the academic year 2020/2021, using a questionnaire developed as a Google form. The sample consisted of 301 and 516 re-registrants from the BTech and BSc programmes respectively. Influential factors were identified using Kruskal Wallis test (for duration of completion), binary logistic regression (for likelihood of completion) and Chi-squared test (associations between presage and process factors).

Entry qualification, age and time management skills at entry had significant effects on duration of completion. Attendance at academic activities, organizing time for self-studies and the competency in English at enrolment had significant effects on the likelihood of completion. Prior open and distance learning (ODL) experience had no significant effect on any of the product factors considered.

Inaccessibility of dropouts and using only the responses from the first administration of the questionnaire are limitations. Active learners are more likely to respond, in the first administration and may bias the results.

Findings are useful for designing future studies to identify at-risk students and thereby enhance the programme completion and reduce prolonged time for completion.

Effective strategies to control the identified factors will uplift programme completion and reduce drop-out rates.

Decision making using inferential techniques makes the study distinct among studies undertaken on the same population. The study enriches the limited current research on factors affecting programme completion in ODL mode.

This research investigates the impact of learners' non-substantive responses in online course forums, referred to as online listening responses, on e-learning performance. A common type of response in online course forums, online listening responses consist of brief, non-substantive replies/comments (e.g. “agree,” “I see,” “thank you,” “me too”) and non-textual inputs (e.g. post-voting, emoticons) in online discussions. Extant literature on online forum participation focuses on learners' active participation with substantive inputs and overlooks online listening responses. This research, by contrast, stresses the value of online listening responses in e-learning and their heterogeneous effects across learner characteristics. It calls for recognition and encouragement from online instructors and online forum designers to support this activity.

The large-scale proprietary dataset comes from a leading MOOC (massive open online courses) platform in China. The dataset includes 68,126 records of learners in five MOOCs during 2014–2018. An ordinary least squares model is used to analyze the data and test the hypotheses.

Online listening responses in course forums, along with learners' substantive inputs, positively influence learner performance in online courses. The effects are heterogeneous across learner characteristics, being more prominent for early course registrants, learners with full-time jobs and learners with more e-learning experience, but weaker for female learners.

This research distinguishes learners' brief, non-substantive responses (online listening responses) and substantive inputs (online speaking) as two types of active participation in online forums and provides empirical evidence for the importance of online listening responses in e-learning. It contributes to online forum research by advancing the active-passive dichotomy of online forum participation to a nuanced classification of learner behaviors. It also adds to e-learning research by generating insights into the positive and heterogeneous value of learners' online listening responses to e-learning outcomes. Finally, it enriches online listening research by introducing and examining online listening responses, thereby providing a new avenue to probe online discussions and e-learning performance.

In a collaborative learning environment there will be many learners with diverse cultures. These learners should be supported to communicate and collaborate among themselves. The variety of the communication and collaboration tools and modes available to each learner would depend on his/her personal cultural background. The purpose of this paper is to suggest the adaptation of the collaborative learning environment to the learner's cultural profile. So, first it aims to present learner's models with respect to his/her cultural characteristics. It also aims to present the various communication and collaboration tools and modes that would be available to the learners. Then, each learner has at his/her disposal the appropriate communication and collaboration tools and modes according to his/her cultural characteristics.

The cultural models of Trompenaars and Hampden‐Turner, as well Hofsted are modified relaxing the dualism of their dimensions. The modified models are used in a collaborative learning environment. The various attributes and types of communication and collaboration among learners and teachers in a collaborative learning environment are also identified.

This paper presents learner's cultural models across several cultural dimensions. Each cultural dimension weights differently. Also, a learner may not belong strictly to a cultural extreme of a dimension, but he/she may have characteristics from both cultural extremes of each dimension. Based on a learner's cultural profile, different communication and collaboration tools would be available to the learner.

Based on the learner's profile, either the adaptation engine, or the teacher, or the learner him/herself may select the appropriate communication and collaboration tools and modes for the particular learner. Designers, developers and evaluators of collaborative learning systems may benefit from these learners' cultural models and the communication and collaboration attributes. For example, they may create collaborative learning systems with flexible communication and collaboration attributes that provide to each learner personalized communication and collaboration tools according to his cultural profile.

This paper proposes the adaptation of the collaborative learning environment to the cultural characteristics of the learner. Future research may assign the specific communication and collaboration tools to each particular learner's cultural profile.

This paper proposes the adaptation of the communication and collaboration tools and modes that are used by a learner in a collaborative learning environment to the learner's cultural characteristics. First, the paper presents new cultural models of a learner. Then, it presents the communication and collaboration attributes and types that would be used by the learners in a collaborative learning environment. A learner would have at disposal the appropriate personalized communication and collaboration tools.