Student use of out-of-class study groups in an introductory undergraduate biology course

Self-formed out-of-class study groups may benefit student learning; however, few researchers have quantified the relationship between study group use and achievement or described changes in study group usage patterns over a semester. We related study group use to performance on content exams, explored patterns of study group use, and qualitatively described student perceptions of study groups. A pre- and posttest were used to measure student content knowledge. Internet-based surveys were used to collect quantitative data on exam performance and qualitative data on study group usage trends and student perceptions of study groups. No relationship was found between gains in content knowledge and study group use. Students who participated in study groups did, however, believe they were beneficial. Four patterns of study group use were identified: students either always (14%) or never (55%) used study groups, tried but quit using them (22%), or utilized study groups only late in the semester (9%). Thematic analysis revealed preconceptions and in-class experiences influence student decisions to utilize study groups. We conclude that students require guidance in the successful use of study groups. Instructors can help students maximize study group success by making students aware of potential group composition problems, helping students choose group members who are compatible, and providing students materials on which to focus their study efforts.     

Psycho-Education with Asylum Seekers and Survivors of Torture

This article examines psycho-educational programmes for asylum-seekers and tortured refugees at the Danish Red Cross Asylum Department and Rehabilitation and Research Centre for Torture victims respectively. The psycho-education programme is based on a cognitive theoretical framework. However, it is argued that the processes observed during the programmes and the changes in the participants’ lives may be conjointly understood within theoretical frameworks of narrative therapy, social constructionism, and community psychology, emphasizing professionals’ and participants’ co-construction of alternative stories and action possibilities, and the importance of the context and social network outside the intervention context.

Pedagogical possibilities for unruly bodies

Project Re?Vision uses disability arts to disrupt stereotypical understandings of disability and difference that create barriers to healthcare. In this paper, we examine how digital stories produced through Re?Vision disrupt biopedagogies by working as body-becoming pedagogies to create non-didactic possibilities for living in/with difference. We engage in meaning making about eight stories made by women and trans people living with disabilities and differences, with our interpretations guided by the following considerations: what these stories ‘teach’ about new ways of living with disability; how these stories resist neoliberalism through their production of new possibilities for living; how digital stories wrestle with representing disability in a culture in which disabled bodies are on display or hidden away; how vulnerability and receptivity become ‘conditions of possibility’ for the embodiments represented in digital stories; and how curatorial practice allows disability-identified artists to explore possibilities of ‘looking back’ at ableist gazes.     

Using natural language processing to support peer-feedback in the age of artificial intelligence: A cross-disciplinary framework and a research agenda

Advancements in artificial intelligence are rapidly increasing. The new-generation large language models, such as ChatGPT and GPT-4, bear the potential to transform educational approaches, such as peer-feedback. To investigate peer-feedback at the intersection of natural language processing (NLP) and educational research, this paper suggests a cross-disciplinary framework that aims to facilitate the development of NLP-based adaptive measures for supporting peer-feedback processes in digital learning environments. To conceptualize this process, we introduce a peer-feedback process model, which describes learners' activities and textual products. Further, we introduce a terminological and procedural scheme that facilitates systematically deriving measures to foster the peer-feedback process and how NLP may enhance the adaptivity of such learning support. Building on prior research on education and NLP, we apply this scheme to all learner activities of the peer-feedback process model to exemplify a range of NLP-based adaptive support measures. We also discuss the current challenges and suggest directions for future cross-disciplinary research on the effectiveness and other dimensions of NLP-based adaptive support for peer-feedback. Building on our suggested framework, future research and collaborations at the intersection of education and NLP can innovate peer-feedback in digital learning environments.

Practitioner notes

What is already known about this topic

• There is considerable research in educational science on peer-feedback processes.
• Natural language processing facilitates the analysis of students' textual data.
• There is a lack of systematic orientation regarding which NLP techniques can be applied to which data to effectively support the peer-feedback process.

What this paper adds

• A comprehensive overview model that describes the relevant activities and products in the peer-feedback process.
• A terminological and procedural scheme for designing NLP-based adaptive support measures.
• An application of this scheme to the peer-feedback process results in exemplifying the use cases of how NLP may be employed to support each learner activity during peer-feedback.

Implications for practice and/or policy

• To boost the effectiveness of their peer-feedback scenarios, instructors and instructional designers should identify relevant leverage points, corresponding support measures, adaptation targets and automation goals based on theory and empirical findings.
• Management and IT departments of higher education institutions should strive to provide digital tools based on modern NLP models and integrate them into the respective learning management systems; those tools should help in translating the automation goals requested by their instructors into prediction targets, take relevant data as input and allow for evaluating the predictions.