How the study of online collaborative learning can guide teachers and predict students' performance in a medical course.

BACKGROUND: Collaborative learning facilitates reflection, diversifies understanding and stimulates skills of critical and higher-order thinking. Although the benefits of collaborative learning have long been recognized, it is still rarely studied by social network analysis (SNA) in medical education, and the relationship of parameters that can be obtained via SNA with students' performance remains largely unknown. The aim of this work was to assess the potential of SNA for studying online collaborative clinical case discussions in a medical course and to find out which activities correlate with better performance and help predict final grade or explain variance in performance. METHODS: Interaction data were extracted from the learning management system (LMS) forum module of the Surgery course in Qassim University, College of Medicine. The data were analyzed using social network analysis. The analysis included visual as well as a statistical analysis. Correlation with students' performance was calculated, and automatic linear regression was used to predict students' performance. RESULTS: By using social network analysis, we were able to analyze a large number of interactions in online collaborative discussions and gain an overall insight of the course social structure, track the knowledge flow and the interaction patterns, as well as identify the active participants and the prominent discussion moderators. When augmented with calculated network parameters, SNA offered an accurate view of the course network, each user's position, and level of connectedness. Results from correlation coefficients, linear regression, and logistic regression indicated that a student's position and role in information relay in online case discussions, combined with the strength of that student's network (social capital), can be used as predictors of performance in relevant settings. CONCLUSION: By using social network analysis, researchers can analyze the social structure of an online course and reveal important information about students' and teachers' interactions that can be valuable in guiding teachers, improve students' engagement, and contribute to learning analytics insights.

How learning analytics can early predict under-achieving students in a blended medical education course.

AIM: Learning analytics (LA) is an emerging discipline that aims at analyzing students' online data in order to improve the learning process and optimize learning environments. It has yet un-explored potential in the field of medical education, which can be particularly helpful in the early prediction and identification of under-achieving students. The aim of this study was to identify quantitative markers collected from students' online activities that may correlate with students' final performance and to investigate the possibility of predicting the potential risk of a student failing or dropping out of a course. METHODS: This study included 133 students enrolled in a blended medical course where they were free to use the learning management system at their will. We extracted their online activity data using database queries and Moodle plugins. Data included logins, views, forums, time, formative assessment, and communications at different points of time. Five engagement indicators were also calculated which would reflect self-regulation and engagement. Students who scored below 5% over the passing mark were considered to be potentially at risk of under-achieving. RESULTS: At the end of the course, we were able to predict the final grade with 63.5% accuracy, and identify 53.9% of at-risk students. Using a binary logistic model improved prediction to 80.8%. Using data recorded until the mid-course, prediction accuracy was 42.3%. The most important predictors were factors reflecting engagement of the students and the consistency of using the online resources. CONCLUSIONS: The analysis of students' online activities in a blended medical education course by means of LA techniques can help early predict underachieving students, and can be used as an early warning sign for timely intervention.

Experiments in computing: a survey.

Experiments play a central role in science. The role of experiments in computing is, however, unclear. Questions about the relevance of experiments in computing attracted little attention until the 1980s. As the discipline then saw a push towards experimental computer science, a variety of technically, theoretically, and empirically oriented views on experiments emerged. As a consequence of those debates, today's computing fields use experiments and experiment terminology in a variety of ways. This paper analyzes experimentation debates in computing. It presents five ways in which debaters have conceptualized experiments in computing: feasibility experiment, trial experiment, field experiment, comparison experiment, and controlled experiment. This paper has three aims: to clarify experiment terminology in computing; to contribute to disciplinary self-understanding of computing; and, due to computing's centrality in other fields, to promote understanding of experiments in modern science in general.

How Text-to-Image Generative AI Is Transforming Mediated Action.

This article examines the intricate relationship between humans and text-to-image generative models (generative artificial intelligence/genAI) in the realm of art. The article frames that relationship in the theory of mediated action-a well-established theory that conceptualizes how tools shape human thoughts and actions. The article describes genAI systems as learning, cocreating, and communicating, multimodally capable hybrid systems that distill and rely on the wisdom and creativity of massive crowds of people and can sometimes surpass them. Those systems elude the theoretical description of the role of tools and locus of control in mediated action. The article asks how well the theory can accommodate both the transformative potential of genAI tools in creative fields and art, and the ethics of the emergent social dynamics it generates. The article concludes by discussing the fundamental changes and broader implications that genAI brings to the realm of mediated action and, ultimately, to the very fabric of our daily lives.

Robustness and rich clubs in collaborative learning groups: a learning analytics study using network science.

Productive and effective collaborative learning is rarely a spontaneous phenomenon but rather the result of meeting a set of conditions, orchestrating and scaffolding productive interactions. Several studies have demonstrated that conflicts can have detrimental effects on student collaboration. Through the application of network science, and social network analysis in particular, this learning analytics study investigates the concept of group robustness; that is, the capacity of collaborative groups to remain functional despite the withdrawal or absence of group members, and its relation to group performance in the frame of collaborative learning. Data on all student and teacher interactions were collected from two phases of a course in medical education that employed an online learning environment. Visual and mathematical analysis were conducted, simulating the removal of actors and its effect on the group's robustness and network structure. In addition, the extracted network parameters were used as features in machine learning algorithms to predict student performance. The study contributes findings that demonstrate the use of network science to shed light on essential elements of collaborative learning and demonstrates how the concept and measures of group robustness can increase understanding of the conditions of productive collaborative learning. It also contributes to understanding how certain interaction patterns can help to promote the sustainability or robustness of groups, while other interaction patterns can make the group more vulnerable to withdrawal and dysfunction. The finding also indicate that teachers can be a driving factor behind the formation of rich clubs of well-connected few and less connected many in some cases and can contribute to a more collaborative and sustainable process where every student is included.

How social network analysis can be used to monitor online collaborative learning and guide an informed intervention.

To ensure online collaborative learning meets the intended pedagogical goals (is actually collaborative and stimulates learning), mechanisms are needed for monitoring the efficiency of online collaboration. Various studies have indicated that social network analysis can be particularly effective in studying students' interactions in online collaboration. However, research in education has only focused on the theoretical potential of using SNA, not on the actual benefits they achieved. This study investigated how social network analysis can be used to monitor online collaborative learning, find aspects in need of improvement, guide an informed intervention, and assess the efficacy of intervention using an experimental, observational repeated-measurement design in three courses over a full-term duration. Using a combination of SNA-based visual and quantitative analysis, we monitored three SNA constructs for each participant: the level of interactivity, the role, and position in information exchange, and the role played by each participant in the collaboration. On the group level, we monitored interactivity and group cohesion indicators. Our monitoring uncovered a non-collaborative teacher-centered pattern of interactions in the three studied courses as well as very few interactions among students, limited information exchange or negotiation, and very limited student networks dominated by the teacher. An intervention based on SNA-generated insights was designed. The intervention was structured into five actions: increasing awareness, promoting collaboration, improving the content, preparing teachers, and finally practicing with feedback. Evaluation of the intervention revealed that it has significantly enhanced student-student interactions and teacher-student interactions, as well as produced a collaborative pattern of interactions among most students and teachers. Since efficient and communicative activities are essential prerequisites for successful content discussion and for realizing the goals of collaboration, we suggest that our SNA-based approach will positively affect teaching and learning in many educational domains. Our study offers a proof-of-concept of what SNA can add to the current tools for monitoring and supporting teaching and learning in higher education.