Journal of Computing in Higher Education

This study presents a scientometric analysis of educational technology research through examining highly cited articles published between 2014 and 2023 in 19 SSCI-indexed Q1 journals. Using a weighted approach to address citation bias, we analyzed 1,770 highly cited articles through document co-citation analysis, keyword analysis, and abstract content analysis. The findings reveal eight distinct research clusters, with Technology Acceptance Model, Computational Thinking, and Classroom Approach emerging as dominant clusters. The analysis identifies five major research themes, with AI-Enhanced Learning Technologies comprising 39% of the research focus, followed by equal distribution (17% each) among Virtual Learning Environments, Digital Learning Practices, and Learning Assessment & Feedback, while Educational Technology Integration accounts for 11%. Keyword analysis further indicates the field’s evolution toward more sophisticated technological applications such as virtual, online learning, and learning analytics emerging as prominent terms. This study demonstrates a significant transformation from basic technology integration to advanced AI-driven solutions. The findings provide valuable insights for researchers and practitioners in educational technology, suggesting future research directions should focus on AI integration, immersive technologies, and data-driven approaches while maintaining emphasis on pedagogical effectiveness and student engagement.

Abstract

The use of ill-structured case examples as an instructional strategy to teach ethical lessons is well-supported in the literature, however, case examples often lack an emotional or affective component. Given the importance of crafting cases for learners, more research is needed to better understand how to construct and present case examples to enhance learning outcomes, specifically related to the influence of emotive content. This study was conducted to assess the effect of emotive content on knowledge acquisition and ethical sense-making. The study employed a posttest-only control group design. Emotive content was defined as information related to the character’s emotional reactions or feelings, background, beliefs, physical appearance, and/or goal focus of the character. Participants were 71 graduate-level Master of Social Work students at a university in the coastal U.S. Results contribute to the growing body of literature regarding the effect of emotion in processing and manipulating complex information. The results suggest that the addition of emotive content to a case example may distract or overwhelm learners. Case examples should be constructed using clear and simple information.

To meet the demands of 21st-century societies and economies, faculty across disciplines must engage college and university students with course activities and assignments that foster the development of computational thinking (CT) skills. Toward this end, examining the ways in which CT can be infused into general education courses has been a topic of recent research. However, the question remains about how students in non-computer science courses can use CT skills in course assignments across disciplines. Guided by a rubric aimed to evaluate the development of CT skills including decomposition, algorithms, data, and abstraction, we examined 101 student-generated artifacts in undergraduate courses across four disciplines: mathematics, sociology, music, and English. In this work, we report on assignment prompts and overall CT skills exhibited by participating students. While some disciplines may not fully facilitate the development of all CT skills, a range of these skills was reflected in student artifacts. We present representative examples demonstrating CT skill development across various levels, including capstone level (score 4), milestone (score 3), benchmark (score 1), and no usage (score 0). The findings of this work provide insights into ways in which higher education faculty can design assignment prompts that support and scaffold students’ development of CT skills, as well as how students across disciplines respond to CT prompts. Findings also have implications for the design of CT-related assessment instruments.

Given the importance of self-regulated learning (SRL) in flipped learning in higher education, this study explored the role of a mobile-based artificial intelligence (AI) chatbot in enhancing SRL among university students enrolled in a flipped business course. The chatbot supported students by providing SRL prompts in the forethought, performance, and reflection phases. An explanatory sequential mixed-methods design was employed to examine the effectiveness of the chatbot and students’ conversation patterns. Survey data from 43 participants revealed that low prior-SRL students significantly benefited from chatbot interaction, while high prior-SRL students surprisingly exhibited a decrease in their SRL scores. Qualitative analysis of extreme cases revealed evident differences in interaction patterns between students whose SRL scores decreased and increased after chatbot use. The findings contribute valuable insights to the expanding field of mobile-based AI chatbots in flipped learning and emphasize the importance of adaptive and personalized interventions for students according to their prior SRL skills.

Flexible online distance education enables students to interact with content and materials at their own pace and from any location. However, such individualization of students’ learning time and space masks differences between learners’ access to resources within their spatial environments and temporal contexts and, thus, might generate implicit forms of social inequity. This study examines how flexibility inherent to emergency remote online learning shapes how Israeli university students from different social groups experienced remote online learning during the Covid-19 pandemic. We thematically analyzed semi-structured interviews with 50 undergraduate and graduate students, representing diversity in terms of class, gender, and national categories. We found four spatial and temporal factors that shaped students’ ability to harness flexibility to benefit their emergency remote online learning: spatial capital, temporal capital, temporal agency, and temporal intensity. The analysis revealed how such factors were shaped by complex intersections between students’ social identities. This study suggests that higher education institutions should make flexibility inclusive and safeguard students from potential adverse effects by tailoring support to diverse student needs and ensuring consistent access to resources as needed.

This study fills a gap in knowledge regarding experienced instructors’ use of learning analytics, focusing on differences in their approach, the knowledge and skills they activate, and the development of these knowledge and skills. Through a qualitative analysis of think-aloud interviews with 13 analytics-experienced instructors, two distinct profiles of analytics use emerged. Instructors in the first profile prioritized monitoring student engagement and performance to foster desirable behaviors, using analytics to align students with course expectations. Instructors in the second profile focused on understanding student perceptions of learning, aligning the course design with diverse learning behaviors and needs. To arrive at such use, instructors went beyond mere acquisition of technical knowledge to also integrate pedagogical knowledge into their analytics practices. Lastly, the study uncovered specific learning analytics supports, such as ongoing individual consultations, invaluable for developing the needed technical and pedagogical knowledge. Together, the results of this study reveal the pivotal role of pedagogy in analytics use, calling for refinement of conceptual models and tailoring of practical support for instructors.

Utilizing a framework for systematic replication coupled with fidelity of implementation, we conducted a distal conceptual replication of a published intervention study that was originally completed with sixth and ninth-grade participants to better understand the critical features and transferability to preservice elementary teachers. The intervention involved learning about cardiac structure and function in virtual reality (VR) with 3-D representations and haptic-enabled feedback. We intentionally manipulated the setting, duration, and arrangement of participants, but used the same methodology. Results support learning about heart anatomy, function, and blood flow (p < 0.01), with an increase of 11.6% and a large effect size (d = 1.02). A similar change was documented for sixth-grade students (13.3%), but was much less than the 26.6% change for ninth-grade students, suggesting a conditional effect for prior knowledge since the content is a seventh-grade standard. Completion and accuracy of the process prompts during the intervention appear strongly related to participant outcomes and misconceptions are consistent with those reported previously. The results do not support claims about the education level and setting as important structural features, but dosage does have an effect and warrants further study. To fully realize the potential of VR for science education, effective models are needed and this study is a step in that direction.

Disabled students are increasingly choosing to attend higher education. Online learning has the potential to meet the needs of disabled students in unique ways. However, questions remain about how well online learning is meeting the needs of disabled students and in what ways institutions are prepared for and actively providing students with accessible and inclusive online learning opportunities. This paper presents the results of a literature review of 91 sources focused on accessible and inclusive online learning. Themes that emerged from the literature are discussed, as are future implications for research and practice.

The purpose of this study is to investigate the structural relationship among the job demands and job resources of institutions regarding online teaching, faculty’s emotional exhaustion and autonomous motivation in online teaching, and their satisfaction in teaching. Since the pandemic, regardless of the capacities of universities where faculty work, there has been an increasing sense that the quality of online education and institutional decisions to offer more online courses and programs would be one of the most significant strategic steps for the universities. This has brought new job demands and at the same time new resources, for faculty’s work in the area of teaching. Situated in the Job Demands-Resources (JD-R) model, we collected survey data from 261 faculty members at US higher education institutions who teach online courses. The findings indicate the significant effect of job demands associated with online teaching on faculty’s emotional exhaustion. The study also highlights the mediating role of faculty exhaustion from online teaching on the relationship between job demands and satisfaction with their teaching. Moreover, emotional exhaustion mediates the relationship between job demands and motivation for online teaching. This study contributes to the current literature by sharing insights on the motivation and well-being of university faculty who face constantly changing job demands and workplaces. Implications for future research and practices to extend and apply the findings are discussed.

Educational Data Mining (EDM) has recently received significant attention, leading to the development of various Data Mining (DM) methodologies for extracting hidden knowledge within educational data. This knowledge is crucial for enhancing teaching methods and improving student learning experiences, ultimately contributing to better student performance and overall educational outcomes. Students confront difficulties in selecting appropriate courses and suitable departments, which is regarded as the most important factor in avoiding career failure. Predicting students’ academic performance is vital for evaluating the success of educational institutions. In this study, eleven Machine Learning (ML) algorithms and three Deep Learning (DL) algorithms namely Support Vector Classification (SVC), K-Nearest Neighbor (KNN), Logistic regression (LR), Decision tree (DT), Linear discriminant analysis (LDA), Quadratic Discriminant Analysis (QDA), Random Forest (RF), Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (Light GBM), Extra Trees, Deep Artificial Neural Network (DANN), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM), were evaluated using real dataset from the Faculty of Computers and Information Sciences (FCIS) at Mansoura University (MU). A prediction model was developed to predict students’ academic grades in upcoming courses based on their past performance, alongside a recommendation model for guiding students towards suitable courses and departments. The results demonstrate that the Support Vector Classification (SVC) model outperformed others, achieving a 78.04% multi-classification accuracy and a 75.37% F1-Score. This study underscores the potential of individual ML and DL models to predict students’ academic performance based on real dataset features.

Digital technologies hold considerable promise for fostering both active and self-directed learning (SDL). However, understanding of how and to what extent educators employ them in higher education to meet this dual objective is still in its nascent stages. This study explored techno-pedagogical competencies among college educators for facilitating active and SDL. Techno-pedagogical competencies refer to the integration of technological skills with pedagogical knowledge that educators require to effectively facilitate learning with digital technologies. We also examined the educator’s perceptions of SDL involving the use of digital technologies. The research population comprised 156 educators from diverse academic disciplines across five Israeli teaching colleges. Utilizing a questionnaire, tailored for this study, and semi-structured interviews, the research reveals that the educators deployed digital technologies primarily to enhance their instructional methods and foster active learning and collaboration among students. Notably, only a limited number of the educators actively encouraged SDL by involving students in the selection and adaptation of digital tools. The interview findings revealed that their pedagogical perspectives on SDL often lacked depth and that they did not provide their students with choices. However, participants underscored the pivotal role of group and collaborative learning within digital contexts as a mechanism to foster SDL, unveiling an interesting perspective. To foster active and SDL it is vital to enhance technological skills while simultaneously fostering a pedagogical understanding of the significance of direct learners’ involvement in the use and selection of digital tools adapted to their educational needs. This dual approach is essential for the effective implementation of active and SDL methodologies within digital teaching environments.

Extensive studies have been conducted to examine the effectiveness of the personalized games on students’ engagement and flow. However, engagement and flow were examined separately rather than concurrently in previous studies. In fact, engagement and flow are two interrelated entities and share similarities but the occurrence differs. How the interrelationship between engagement and flow impacts student learning in the personalized gaming environment has been seldomly investigated. Aiming to fill this gap, this study examined the impact of personalization on student engagement and flow concurrently within a gaming environment. The results showed that personalization had a significant impact on students’ engagement, specifically students’ focused attention. However, a significant difference in flow was not observed. Flow (primarily concentration, autotelic experience, and loss of self-consciousness) exhibited a strong correlation with engagement (primarily focused attention). The findings indicated that the personalization game feature could be used to engage students but needs to incorporate additional non-game features, such as instructional features, to promote the flow.

The present study conducted two experiments to investigate the impact of pedagogical agent’s emotional expression on multimedia learning from multi-faceted perspectives. Experiment 1 explored the overall impact of the pedagogical agent’s emotional expression (positive vs. neutral) on learning from a macro perspective. The positive pedagogical agent (PPA) had a smiling face and an enthusiastic voice, while the neutral pedagogical agent (NPA) displayed a neutral facial expression and a neutral voice. Sixty undergraduate students were randomly assigned to one of two conditions. Results showed that compared to NPA, PPA increased enjoyment but also induced frustration. Furthermore, it had no significant impact on intrinsic motivation or learning performance. Building on the results of Experiment 1, Experiment 2 explored the dynamic impact of the pedagogical agent’s emotional expression (positive vs. neutral) on learning processes from a micro perspective. Results showed that the dynamic impact of the pedagogical agent’s emotional expression was emotional-specific: PPA had an obvious advantage over NPA only when learners were enjoyable. However, presenting a PPA when learners were surprised could hinder learning. When learners felt frustrated and confused, PPA and NPA had their own advantages and disadvantages. Learners could benefit from both types of pedagogical agents when their emotions were neutral but disengaged from learning when they were bored. Therefore, pedagogical agents should identify learners’ emotions in real time and exhibit positive emotions on appropriate occasions.

Task complexity emerges as one of the factors affecting the computer-supported collaborative learning (CSCL) process, group dynamics, and processes. Upon reviewing the literature, it becomes apparent that there are conflicting results regarding the impact of escalating task complexity on group dynamics and collaboration processes. The lack of clear findings in the literature on how task complexity affects group processes in CSCL makes instructors uncertain about how to adjust the level of task complexity in CSCL processes. This uncertainty may lead to the negative effects of assigning tasks that are too difficult or too easy on group processes. The starting point of this research is whether giving complex tasks in CSCL improves group dynamics and collaboration. This study aimed to explore the effects of task complexity on group atmosphere, group cohesion, and the group’s transactive memory system in CSCL. One distinctive feature of this research within the educational context is the utilization of Zoom as the platform for CSCL. Although Zoom is used extensively for cooperative learning, especially after the pandemic, less research is examining its use in terms of CSCL. Zoom has been widely used due to its ease of use, high speed, and capacity, and CSCL features such as a whiteboard. However, there is a need for research in the literature regarding the collaborative learning process and outcomes using Zoom as a CSCL tool. The research was conducted on 166 university students studying in the field of computer science education. The analysis was carried out in the programming education course, which was taught with the CSCL method. Self-report scales of perceived task complexity, group atmosphere, group cohesion, and transactive memory were used in the research. Path analysis was performed in the study of the data. Research findings show that perceived task complexity greatly influences the transactive memory system. As task complexity decreases, the group's transactive memory system develops. It was seen that the transactive memory system had no effect on group cohesion, and group cohesion had a significant effect on the group atmosphere. Depending on the increase in group cohesion, the group atmosphere develops. Based on the research findings, several recommendations were provided for researchers and educators to manage the structure and processes of CSCL groups and to adjust task complexity.

The rise of online and hybrid learning in universities, especially due to the pandemic, has not only revolutionized how students learn, but also blurred the boundaries between work, school, and leisure activities because of the pervasive use of technology surrounding students in their daily lives. The purpose of this study is to investigate, using coping theory, the relationships among academic self-efficacy, academic procrastination, and techno-overload, three constructs especially crucial in the current education climate. Using a sample of 179 undergraduate students, we found that techno-overload partially mediates the relationship between academic self-efficacy and academic procrastination. This finding demonstrates that techno-overload helps further explain how academic self-efficacy is negatively associated with procrastination. Specifically, academic self-efficacy is negatively related to techno-overload which is then positively linked to procrastination. Consistent with coping theory, our findings highlight the importance of mitigating technology-related stress in order to reduce academic procrastination. Implications and future research directions are discussed.

Providing pre-training on new material can simplify complex content for learners who may need guidance to understand basic facts and organize their efforts. However, the effect of pre-training on learning outcomes is controversial because it tends to vary by context. Our aim was to investigate the effectiveness of pre-training in reducing intrinsic cognitive load and improving learning in a computer-based learning environment. Predictors of learning were also investigated. A total of 384 undergraduate students were randomly assigned to experimental conditions within a Solomon four-group design (n = 293). An additional manipulation control group (n = 91) was also included to determine the effectiveness of the current pre-training activity in improving achievement scores. Results showed significant relationships between cognitive load and achievement. Intrinsic and extraneous cognitive load were negatively correlated with performance, while germane cognitive load was positively correlated. Cognitive load explained 20% of the variance in achievement. In addition, topic interest and achievement motivation were associated with cognitive load. Interestingly, despite evidence of the usefulness of pre-training from the manipulation control group, it did not significantly affect learning gains. That is, all groups exposed to the video instruction had significantly better post-test scores, but pre-training did not make a difference between the groups. However, pre-training did appear to be somewhat effective in reducing intrinsic cognitive load. Findings underscore the importance of considering cognitive load dynamics and motivational factors in instructional design strategies aimed at optimizing learning experiences and outcomes for undergraduate students.

This multiple case study explored the differences in online course design across disciplines using the Community of Inquiry (CoI) framework as a lens. While the CoI framework was developed to be generic, research on the CoI and disciplinary differences has called for changes to the framework, yet none of the studies have considered the instructors’ design decisions. Twelve cases were selected to represent the four disciplinary categories per Biglan’s framework (1973): hard-applied (n = 3), soft-applied (n = 3), soft-pure (n = 3), and hard-pure (n = 3); each case was a combination of instructor/teaching assistant and course, allowing us to deeply examine the course design elements and juxtapose the implemented design with the instructors’ reasoning behind their design decisions. Data sources consisted of pre-interview surveys, semi-structured interviews, and online course observations. A general case analytic strategy and a cross-case synthesis technique were employed to (a) construct case descriptions informed by the CoI framework and (b) compile and analyze patterns both within and across disciplines. The results indicated that CoI was a fit across all four disciplinary areas, regardless of instructors’ intention to purposefully design for a CoI. However, differences were observed in how indicators were applied within disciplines potentially due to differing values and emphasis of each discipline and individual instructor. This study has implications for both research and practice. Further research is needed to consider meaningful strategies for each CoI presence that are aligned with specific disciplinary practices while simultaneously updating the indicators traditionally used to represent the framework. The examples of different CoI indicators resulting from this research offer many useful possibilities for instructors to consider.