Individual Learning Development Research Articles

ChatGPT: a force for good? Using the I.D.E.As framework to ’develop and empower’ students

Generative artificial intelligence (GenAI) tools (for example, ChatGPT) are a specific application of artificial intelligence which generate novel content in response to questions or instructions. Their use in education has generated diverse responses. Critics express concerns that they might discourage students from engaging in independent analysis, evaluation and problem-solving, and hinder the development of critical thinking skills (Farrokhnia et al., 2023). Another concern is the potential effect on the development of meaningful student-teacher and peer-to-peer interactions, important in fostering student belonging, and developing communication and social-emotional skills. Conversely, supporters see these tools as valuable resources providing instant access to tailored information, facilitating personalised learning experiences and promoting student engagement. They argue that rather than undermining higher-order skills like critical thinking and evaluative judgment, GenAI tools can enhance their development. Some propose they may even help address the ‘awarding gap’ (Fido and Wallace, 2023), with students using such tools as a ‘dialogic tutor’, to personalise learning and so develop their knowledge and understanding (Compton, 2023a). The presentation introduced the ‘I.D.E.As framework’ (Hack, 2023), a resource developed to help structure thinking and curate ideas regarding using GenAI tools in learning and teaching. The session explored ways in which GenAI tools may be used as a ‘force for good’, to enhance the student learning experience and support their individual learning development. Resources to ‘develop and empower’ students as independent learners were shared, providing an opportunity for attendees to reflect on how/whether they might be incorporated into their own learning and teaching contexts.

An online integrated programming platform to acquire students' behavior data for immediate feedback teaching

AbstractProgramming‐related courses prefer to put importance on practice rather than theoretical knowledge. However, the students' programming behavior data is difficult to be recorded and fed back to teachers anytime, allowing teachers to adjust their teaching in real‐time. Aiming at this problem, this study develops an online integrated programming platform that provides a unified programming environment for teachers and students and logs students' procedural programming behavior data automatically to assist teachers in providing real‐time feedback teaching. To verify the effectiveness of the data‐driven immediate feedback teaching method, an exploratory case was conducted in the 2020/2021 academic year autumn semester. As the control group, cloud201 adopted the traditional hybrid online/offline mode, while cloud202, as the experimental group, adopted the hybrid online/offline mode and immediate feedback teaching mode. The effect is demonstrated through the questionnaire, the programming behavior data analysis, and the performance analysis. The results of the questionnaire show that there are significant differences in the students' satisfaction with interaction, supervision, feedback, and evaluation, of which feedback is the biggest part. In addition, the two analysis results indicate that students' individual learning development and the learning status of the class are objectively characterized, the overall grade distribution of cloud202 is more concentrated in medium grades and the difference on average scores between cloud201 and cloud202 is gradually increased. Since the students' immediate and comprehensive programming behavior data is perceived by teachers, the teaching adjustment is so flexible that the improvement of teaching efficiency and teaching effect is promoted accordingly.

Engaging students online: an analysis of students’ motivations for seeking individual learning development support

In the context of increased concerns with student engagement across the Higher Education sector, which have intensified subsequent to the rapid transition to online delivery in March 2020, this small-scale research project aimed to explore the motivations for student engagement in self-selecting learning development (LD) online tutorials. The study used a mixed methods approach, including an online survey (n=43) and online interviews (n=5). The sample comprised undergraduate and postgraduate volunteers recruited from from a pool of LD tutorial users (n=390) within the project timeframe (October 2020-April 2021). The generalisability of findings is limited by the low response rate as well as age bias. The main driver for engagement reported was participants’ limited confidence in their own academic writing abilities, which was consistently linked to attainment. Engagement was further motivated through a range of perceived impacts, including improved confidence, awareness of academic conventions, and higher grades. In this context, the main challenge was limited availability of support. Participants reported a generally positive attitude towards online delivery. Qualitative data from both the survey and interviews were further investigated using a discourse analysis framework. One key finding was that the path to LD engagement is often mediated by academic authority figures, who may exert a significant impact on learner self-views. Key recommendations for learning developers include maximising the potential of lessons learned from the enforced pivoting to online delivery to underpin the developmental dimension of LD, with the ultimate goal of promoting learner confidence and growth.