Journal of Computing in Higher Education

The flipped classroom approach involves restructuring traditional classroom instruction and facilitating students' active involvement with educational resources beyond the classroom setting. This approach allows for in-class activities that promote active learning and collaboration. The present study directed to investigate the influence of the flipped classroom on creative thinking, self-esteem, and social interaction among intermediate-level language learners. However, there has been limited discussion on this topic so far. In 2023, researchers employed a multi-stage cluster sampling technique to select participants. A total of 420 intermediate language learners from various institutions were randomly divided into an experimental group (210 learners) and a control group (210 learners). To ensure participant homogeneity, the Oxford placement test was used to assess the language proficiency of the learners. The research instruments included the Torrance creative thinking scale, the Rosenberg self-esteem scale, and the Garsham and Elliott social interaction scale. Descriptive and inferential statistics were employed to evaluate the data applying SPSS 25 and Amos 24 software. The findings from both descriptive and inferential analyses imply that the flipped classroom approach has a significant impact on creative thinking, self-esteem, and social interaction among English foreign language learners (EFLLs). Language educators are encouraged to adopt specific elements of this approach, such as structured pre-class activities (e.g., video lectures), interactive in-class discussions, and collaborative projects, to create engaging learning environments for EFLLs. Further research should explore the long-term effects of the flipped classroom on language retention and learner motivation, as well as its applicability across various proficiency levels, age groups, and cultural contexts.

As collaborative learning environments, forums in massive open online courses (MOOCs) seek to facilitate knowledge construction though meaningful discussions. Such discussions, however, rarely occur Problems such as difficult navigation, non-interactive participation, and brief interactions hinder discussions in MOOC forums. While pedagogical design holds promise for addressing these issues, few studies have implemented interventions to explore their impact. This paper presents findings from an intervention redesigning the forums in two MOOCs. Using a layered approach, we redesigned 12 forums to improve navigation, promote interactive participation, and increase the length of learners’ interactions. Results show that our intervention significantly reduced forum posts with uninformative titles, thereby improving navigation. Our intervention also helped learners both start and reply to threads, improving the quality of their forum interaction. Lastly, our intervention helped to increase the number of interactions, though interactions were not necessarily longer. These findings highlight the importance of pedagogical design in fostering meaningful discussions in MOOC forums.

Considering both the transformative opportunities and challenges presented by generative AI (GenAI) in academic writing, effectively integrating GenAI into the academic setting becomes a significant need requiring prioritization. Yet, there is limited understanding regarding the nature of interactions between different types of students, what behavioral patterns students exhibit during a student-GenAI interaction (SAI) on a given task, and how these different SAI patterns relate to the actual writing task performance. This study, therefore, aimed to identify SAI patterns of academic writing tasks depending on students’ level of AI literacy and examine the differences in academic writing performance between the identified SAI patterns. Drawing from the combination of three data sources, including think-aloud protocols, screen-recordings, and chat histories between 36 Chinese graduate students and a GenAI writing system, epistemic network analysis (ENA) was used to reveal the distinctive SAI patterns of students with different levels of AI literacy. The study found that students with a high level of AI literacy exhibited a collaborative approach to SAI, actively accepting GenAI’s suggestions and engaging GenAI in meta-cognitive-related activities such as planning, whereas students with a low level of AI literacy demonstrated much less interaction with GenAI in completing their writing tasks, instead choosing to ideate and evaluate independently. In addition, the Wilcoxon rank-sum (Mann-Whitney U) test was conducted to assess the writing task performance of the two AI literacy groups. Findings revealed statistical differences in all evaluation rubrics (content, structure/organization, expression). This study offers implications for the design and implementation of GenAI agents in writing tasks and the pedagogy of GenAI-assisted instruction.

Collaborative knowledge building (CKB) encourages students to build on knowledge at the group level through peer interactions. Process and summative assessments are essential methods for understanding and promoting the quality of CKB, but few studies focus on the assessment at the group level. To provide an operationalizable measurement of CKB process, this research conducted a process and summative assessment of Knowledge Forum data from groups of graduated students, then conducted group classification based on the assessments, and further examined the transitional and developmental characteristics. The research proposes an operationalizable measurement equation for assessing the quality of the CKB at the group level, it also proposes a summative assessment based on the final knowledge artefacts produced by the groups. Group classification was identified based on the results of both the process assessment and the summative assessment. Using the process mining method, this research visualized and demonstrated the procedural details of learning engagement within different group classifications during the CKB processes. The research identified four distinct group classifications based on the process assessment of CKB and the summative assessment of final research proposals. The results of process mining showed that four group classifications exhibited varying transitional and developmental processes in terms of social, cognitive, and metacognitive engagement. Results showed that the high-quality CKB process and performance primarily depended on progressive interactions based on group-level knowledge negotiation or perspective exchange, rather than merely interacting on questioning or information sharing. Three significant pedagogical implications and three assessment implications were proposed.

Collaborative learning is a fundamental skill based on the construction of knowledge through collaborative discussion in order to comprehend diverse perspectives. In online and flipped classrooms, which have become popular in higher education, learning interventions that provide a high level of collaborative cognitive support are required to increase active participation and enhance learning. At this point, there is a need to explain the contribution of socially shared metacognition (SSM) support for effective collaborative work in online and flipped classrooms. This study aims to investigate the effect of online and flipped classes supported by SSM on group metacognition (MCO), group belonging (GB), cohesion, and motivation. For this purpose, an experimental intervention consisting of two sub-studies was conducted with 330 university students. Descriptive statistics and partial least squares-structural equation modeling (PLS-SEM) analyses were employed in the analysis of the data. As a result of the research, when the pretest and posttest results were compared in the group provided with flipped SSM support, it was found that group belonging, metacognition, cohesion, and intrinsic and extrinsic motivation scores showed significant and positive development. In the online SSM-supported group, group cohesion (GC) showed a significant increase in the context of the pretest and posttest scores. In MGA analysis, it was concluded that the path coefficient differentiation of group metacognition was higher in those who received online SSM support. SSM support positively affected the perception of task difficulty in both flipped and online classes.

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.