Approaching Adaptive Support for Self-regulated Learning

Seidel, Niels; Karolyi, Heike; Burchart, Marc; Witt, Claudia de

doi:10.1007/978-3-030-90677-1_39

Cited by 2 publications

(2 citation statements)

References 22 publications

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“…To address our first research question, we assessed learners’ self‐reported application of SRL strategies using the LIST (Learning Strategies of University Students Learning and Study Strategies Inventory) scales (Klingsieck, 2019; Seidel et al., 2021; Wild & Schiefele, 1994), which are based on the MSLQ (Pintrich et al., 1991) and the LASSI questionnaire (Weinstein et al., 1987). The LIST questionnaire was chosen because it includes the dimension of resource management, which addresses the unique challenges faced by adult learners.…”

Section: Methodsmentioning

confidence: 99%

Meeting the challenges of continuing education online courses: Can we promote self‐regulated learning strategies with adaptive support?

Teich,

Loock,

Rummel

2024

Brit J Educational Tech

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Online courses are particularly attractive in continuing education because they offer a high degree of autonomy and flexibility, although this requires learners to take greater responsibility for self‐regulating their learning. That can be challenging for adult learners as they need to handle other obligations in addition to learning. Thus, self‐regulated learning (SRL) support might be needed. In the present study, we aimed to investigate how the specific SRL challenges that adult learners face in online courses can be supported adaptively. Main challenges for adult learners consist in managing learning resources (e.g., time, attention, learning environment). To address these challenges, we developed an adaptive support intervention comprising several features. To assess the effects of the adaptive support on the self‐reported SRL strategy application of adult learners, we analysed data from five iterations of a continuing education online course comparing iterations with adaptive support (with a total of N = 36 participants) to iterations without (with a total of N = 35 participants). Results indicate that the adaptive support significantly improved environmental structuring but did not significantly affect other SRL strategies. Further analyses revealed that learners who did not use the support deteriorated in managing their learning time and in setting specific learning goals during the course. Practitioner notesWhat is already known about this topic Adult learners are likely to face various challenges in self‐regulated online learning, including managing conflicting responsibilities and maintaining focus. Adult learners' successful application of SRL strategies can be challenging, underlining the need for support. Adaptive support interventions have demonstrated positive effects in various educational contexts, indicating potential effectiveness for adult online learners as well. What this paper adds The study provides insights into the positive effects and potential pitfalls of designing an adaptive support intervention to improve adult learners' self‐reported use of the SRL strategies of effort, attention, time management, environmental structuring, and goal setting. Implications for practice and/or policy The findings suggest that providing adaptive support, namely a structured course overview and adaptive recommendations of learning time and relevant content, can positively influence adult learners' environmental structuring. Our findings also highlight the challenge of achieving optimal support design, as evidenced by low support use. This finding highlights the need to explore additional methods to motivate adult learners to use the support provided while carefully respecting their autonomy.

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Section: Methodsmentioning

confidence: 99%

Meeting the challenges of continuing education online courses: Can we promote self‐regulated learning strategies with adaptive support?

Teich,

Loock,

Rummel

2024

Brit J Educational Tech

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“…Системи управління навчанням (LMS), як найбільш поширені системи онлайн-навчання у формальній освіті, забезпечують усім здобувачам освіти однакове навчальне середовище. Різні потреби під час навчання можуть вимагати адаптації навчального середовища з точки зору навчального контенту, інструктивних матеріалів, зворотного зв'язку та різних форм взаємодії між учасниками освітнього процесу [1]. К. Осадча, В. Осадчий, О. Спірін, В. Круглик для підтримки індивідуалізації навчання засобами Moodle рекомендують застосовувати різні налаштування окремих модулів, елементів та ресурсів, такі як: формування змісту дисципліни у вигляді модульної структури; створення багаторівневих тестів; використання інструментів для формування портфоліо; засоби формування маршруту навчання шляхом накладення необхідних обмежень на елементи курсу, відстеження рівня оцінки, багатокритеріального оцінювання; багатоваріантність подання навчальної інформації; налаштування роботи міні груп; використання системи ролей; застосування мобільних технологій навчання [2].…”

Section: модель персоналізації навчання студентів відповідно до навча...unclassified

Модель Персоналізації Навчання Студентів Відповідно До Навчального Стилю Засобами LMS Moodle

Глазунова,

Мокрієв,

Волошина

et al. 2024

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The article is devoted to the study of the possibilities of Moodle tools for personalizing student learning in higher education institutions, taking into account different learning styles. Given the diversity of students' characteristics, the authors explore the opportunities provided by Moodle to create a learning environment adapted to different learning styles (auditory (A), visual (V), read/write (R), kinesthetic (K)). A wide range of resources and activities are available in the Moodle LMS, which can be used to create and place educational content for students in a convenient format for each learning style. The conceptual architecture of the personalized learning system for students based on three models is developed and described: the model of the learner (student), the model of educational content, and the model of personalized learning. The article presents an object model of a personalized learning system for tracking students' progress in the learning process, which consists of the following components: students; learning styles; trajectories; content type; resource types; test attempts; tests; and resources.

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Approaching Adaptive Support for Self-regulated Learning

Cited by 2 publications

References 22 publications

Meeting the challenges of continuing education online courses: Can we promote self‐regulated learning strategies with adaptive support?

Meeting the challenges of continuing education online courses: Can we promote self‐regulated learning strategies with adaptive support?

Модель Персоналізації Навчання Студентів Відповідно До Навчального Стилю Засобами LMS Moodle

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