Teachers’ Learning Profiles in Learning Programming
The Big Picture
Dados Bibliográficos
| ID | 22166398 |
|---|---|
| Autores | Mohammed Saqr (0000-0001-5881-3109, University of Eastern Finland, autor correspondente), Ville Tuominen (autor correspondente), Teemu Valtonen (0000-0002-1803-9865, University of Eastern Finland), Erkko Sointu (0000-0003-4001-7264, University of Eastern Finland), Sanna Väisänen (0000-0002-2981-912X, University of Eastern Finland), Laura Hirsto (0000-0002-8963-3036, University of Eastern Finland) |
| Ano | 2022 |
| Volume | 7 |
| Data de publicação | 2022-05-30 |
| Peer Reviewed | Sim |
| Open Access | Sim |
| Tipo | ARTICLE |
| Periódico | Frontiers in Education (JOURNAL) |
| Identificadores do periódico | ISSN: 2504-284X • E-ISSN: 2504-284X |
| Editora | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/feduc.2022.840178 |
| OpenAlex | W4281665044 |
| Idioma | EN |
| Citações recebidas | 1 |
| Referências citadas | 38 |
Currently there is a need for studying learning strategies within Massive Open Online Courses | (MOOCs), especially in the context of in-service teachers. This study aims to bridge this gap and try to understand how in-service teachers approach and regulate their learning in MOOCs. In particular, it examines the strategies used by the in-service teachers as they study a course on how to teach programming. The study implemented a combination of unsupervised clustering and process mining in a large MOOC ( n = 27,538 of which 8,547 completed). The results show similar trends compared to previous studies conducted within MOOCs, indicating that teachers are similar to other groups of students based on their learning strategies. The analysis identified three subgroups (i.e., clusters) with different strategies: (1) efficient ( n = 3596, 42.1%), (2) clickers ( n = 1785, 20.9%), and (3) moderates ( n = 3,166, 37%). The efficient students finished the course in a short time, spent more time on each lesson, and moved forward between lessons. The clickers took longer to complete the course, repeated the lessons several times, and moved backwards to revise the lessons repeatedly. The moderates represented an intermediate approach between the two previous clusters. As such, our findings indicate that a significant fraction within teachers poorly regulate their learning, and therefore, teacher education should emphasize learning strategies and self-regulating learning skills so that teacher can better learn and transfer their skills to students
Cluster analysis · Mathematics education · Computer Science · E-Learning and Knowledge Management · Online Learning and Analytics · Psychology · Software Engineering Research · Artificial Intelligence
Self‐Regulation and Learning
Methodology and Application of the Kruskal-Wallis Test
Self-regulated learning
Inconvenient truths about teacher learning
A comparative analysis of international frameworks for 21 st century competences
Young children's self-regulated learning and contexts that support it.
Self-Regulated Learning and Academic Achievement
Defining and Measuring Engagement and Learning in Science
Learning analytics to unveil learning strategies in a flipped classroom
Conceptualizing Teacher Professional Learning
NbClust
Deconstructing disengagement
A learning analytics perspective on educational escape rooms
The longitudinal trajectories of online engagement over a full program
Students matter the most in learning analytics
Reliability and Predictive Validity of the Motivated Strategies for Learning Questionnaire (Mslq)
Professional Development and Teacher Learning
Understanding Student Learning
Students' approaches to learning and their experiences of the teaching-learning environment in different disciplines
On Qualitative Differences in Learning
Learning Analytics
| Obras citantes distintas | 1 |
|---|---|
| Citações por ano | 1 |
| Intervalo de citações | 2026 - 2026 (1) |
| Velocidade de citação | current |
| Altamente citado | Não |
| Tipos de citação | Neutras: 1 |