Journal of Computing in Higher Education

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.