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- Li, Xiu, 1982-, et al.
(författare)
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Supporting Teaching-to-the-Curriculum by Linking Diagnostic Tests to Curriculum Goals : Using Textbook Content as Context for Retrieval-Augmented Generation with Large Language Models
- 2024
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Ingår i: Artificial Intelligence in Education. - : Springer Nature. - 9783031643026 - 9783031643019 ; , s. 118-132
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Konferensbidrag (refereegranskat)abstract
- Using AI for automatically linking exercises to curriculum goals can support many educational use cases and facilitate teaching-to-the-curriculum by ensuring that exercises adequately reflect and encompass the curriculum goals, ultimately enabling curriculum-based assessment. Here, we introduce this novel task and create a manually labeled dataset where two types of diagnostic tests are linked to curriculum goals for Biology G7-9 in Sweden. We cast the problem both as an information retrieval task and a multi-class text classification task and explore unsupervised approaches to both, as labeled data for such tasks is typically scarce. For the information retrieval task, we employ SOTA embedding model ADA-002 for semantic textual similarity (STS), while we prompt a large language model in the form of ChatGPT to classify diagnostic tests into curriculum goals. For both task formulations, we investigate different ways of using textbook content as a pivot and provide additional context for linking diagnostic tests to curriculum goals. We show that a combination of the two approaches in a retrieval-augmented generation model, whereby STS is used for retrieving textbook content as context to ChatGPT that then performs zero-shot classification, leads to the best classification accuracy (73.5%), outperforming both STS-based classification (67.5%) and LLM-based classification without context (71.5%). Finally, we showcase how the proposed method could be used in pedagogical practices.
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| 2. |
- Afzaal, Muhammad, 1989-
(författare)
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Explainable AI for Data-Driven Feedback and Intelligent Action Recommendations to Support Student Self-Regulation
- 2024
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Self-regulated learning (SRL) is a cognitive ability with demonstrable significance in facilitating students’ ability to effectively strategize, monitor, and assess their own learning actions. Studies have indicated that a lack of selfregulated learning skills negatively impacts students’ academic performance. Effective data-driven feedback and action recommendations are considered crucial for SRL and significantly influence student learning and performance. However, the task of delivering personalised feedback to every student poses a significant challenge for teachers. Moreover, the task of identifying appropriate learning activities and resources for individualised recommendations poses a significant challenge for teachers, given the large number of students enrolled in most courses.To address these challenges, several studies have examined how learning analytics-based dashboards can support students’ self-regulation. These dashboards offered several visualisations (as feedback) on student success and failure. However, while such feedback may be beneficial, it does not offer insightful information or actionable recommendations to help students improve academically. Explainable artificial intelligence (xAI) approaches have been proposed to explain such feedback and generate insights from predictive models, with a focus on the relevant actions a student needs to take to improve in ongoing courses. Such intelligent activities could be offered to students as data-driven behavioural change recommendations.This thesis offers an xAI-based approach that predicts course performance and computes informative feedback and actionable recommendations to promote student self-regulation. Unlike previous research, this thesis integrates a predictive approach with an xAI approach to analyse and manipulate students’ learning trajectories. The aim is to offer detailed, data-driven actionable feedback to students by providing in-depth insights and explanations for the predictions provided by the approach. The technique provides students with more practical and useful knowledge compared to the predictions alone.The proposed approach was implemented in the form of a dashboard to support self-regulation by students in university courses, and it was evaluated to determine its effects on the students’ academic performance. The results revealed that the dashboard significantly enhanced students’ learning achievements and improved their self-regulated learning skills. Furthermore, it was found that the recommendations generated by the proposed approach positively affected students’ performance and assisted them in self-regulation.
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| 3. |
- Afzaal, Muhammad, et al.
(författare)
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Informative Feedback and Explainable AI-Based Recommendations to Support Students' Self-regulation
- 2024
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Ingår i: Technology, Knowledge and Learning. - 2211-1662 .- 2211-1670. ; 29:1, s. 331-354
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Tidskriftsartikel (refereegranskat)abstract
- Self-regulated learning is an essential skill that can help students plan, monitor, and reflect on their learning in order to achieve their learning goals. However, in situations where there is a lack of effective feedback and recommendations, it becomes challenging for students to self-regulate their learning. In this paper, we propose an explainable AI-based approach to provide automatic and intelligent feedback and recommendations that can support the self-regulation of students' learning in a data-driven manner, with the aim of improving their performance on their courses. Prior studies have predicted students' performance and have used these predicted outcomes as feedback, without explaining the reasons behind the predictions. Our proposed approach is based on an algorithm that explains the root causes behind a decline in student performance, and generates data-driven recommendations for taking appropriate actions. The proposed approach was implemented in the form of a dashboard to support self-regulation by students on a university course, and was evaluated to determine its effects on the students' academic performance. The results revealed that the dashboard significantly enhanced students' learning achievements and improved their self-regulated learning skills. Furthermore, it was found that the recommendations generated by the proposed approach positively affected students' performance and assisted them in self-regulation
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| 4. |
- Wickberg Hugerth, Mattias, et al.
(författare)
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"I Should, but I Don't Feel Like It" : Overcoming Obstacles in Upper Secondary Students' Self-regulation Using Learning Analytics
- 2024
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Ingår i: Studia Paedagogica. - 1803-7437 .- 2336-4521. ; 28:3, s. 89-111
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Tidskriftsartikel (refereegranskat)abstract
- Även om forskning har bedrivits om självreglerat lärande i relation till lärandeanalys finns det fortfarande en kunskapslucka när det gäller de hinder som elever i gymnasieutbildningen möter i att reglera sitt eget lärande och hur lärandeanalys kan stödja deras självreglering. Denna artikel undersöker två frågor: 1) Vilka utmaningar upplever gymnasieelever i processen att reglera sitt eget lärande?, och 2) Vilken information och data behöver gymnasieelever för att bättre kunna reglera sitt eget lärande? Vi genomförde en studie på en medelstor gymnasieskola i Mellansverige för att bättre förstå hur dessa frågor manifesterar sig bland eleverna. Vi analyserade data som samlats in av skolan två gånger årligen mellan 2015 och 2022 och administrerade ett frågeformulär till 224 elever för att besvara forskningsfrågorna. Genom beskrivande statistik och en tematisk analys identifierar vi vanliga problem som elever stöter på samt den information som är nödvändig för att stötta självreglerat lärande. Vi diskuterar implikationerna av våra fynd för utformningen av system som förser elever med relevant data för att förbättra deras lärandeupplevelser.
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