scholarly journals The Role of Self-Improving Tutoring Systems in Fostering Pre-Service Teacher Self-Regulated Learning

2022 ◽  
Vol 4 ◽  
Author(s):  
Lingyun Huang ◽  
Laurel Dias ◽  
Elizabeth Nelson ◽  
Lauren Liang ◽  
Susanne P. Lajoie ◽  
...  
Keyword(s):  
Preservice Teacher ◽  
Language Processing ◽  
Web Mining ◽  
Clickstream Data ◽  
Tutoring Systems ◽  
Collective Behaviors ◽  
Self Regulated Learning ◽  
Regulated Learning ◽  
Ambient Data ◽  
Support Teacher

Computer-based learning environments serve as a valuable asset to help strengthen teacher preparation and preservice teacher self-regulated learning. One of the most important advantages is the opportunity to collect ambient data unobtrusively as observable indicators of cognitive, affective, metacognitive, and motivational processes that mediate learning and performance. Ambient data refers to teacher interactions with the user interface that include but are not limited to timestamped clickstream data, keystroke and navigation events, as well as document views. We review the claim that computers designed as metacognitive tools can leverage the data to serve not only teachers in attaining the aims of instruction, but also researchers in gaining insights into teacher professional development. In our presentation of this claim, we review the current state of research and development of a network-based tutoring system called nBrowser, designed to support teacher instructional planning and technology integration. Network-based tutors are self-improving systems that continually adjust instructional decision-making based on the collective behaviors of communities of learners. A large part of the artificial intelligence resides in semantic web mining, natural language processing, and network algorithms. We discuss the implications of our findings to advance research into preservice teacher self-regulated learning.

scholarly journals nStudy: Software for Learning Analytics about Processes for Self-Regulated Learning

2019 ◽  
Vol 6 (2) ◽  
Author(s):  
Philip H Winne ◽  
Kenny Teng ◽  
Daniel Chang ◽  
Michael Pin-Chuan Lin ◽  
Zahia Marzouk ◽  
...  
Keyword(s):  
Learning Analytics ◽  
Learning Science ◽  
Self Directed Learning ◽  
Self Regulated Learning ◽  
Regulated Learning ◽  
Directed Learning ◽  
Ambient Data ◽  
Learning As A Process ◽  
Learning Event

Data used in learning analytics rarely provide strong and clear signals about how learners process content. As a result, learning as a process is not clearly described for learners or for learning scientists. Gašević, Dawson, and Siemens (2015) urged data be sought that more straightforwardly describe processes in terms of events within learning episodes. They recommended building on Winne’s (1982) characterization of traces — ambient data gathered as learners study that more clearly represent which operations learners apply to which information — and his COPES model of a learning event — conditions, operations, products, evaluations, standards (Winne, 1997). We designed and describe an open source, open access, scalable software system called nStudy that responds to their challenge. nStudy gathers data that trace cognition, metacognition, and motivation as processes that are operationally captured as learners operate on information using nStudy’s tools. nStudy can be configured to support learners’ evolving self-regulated learning, a process akin to personally focused, self-directed learning science.

Development of the Teacher Attitudes Towards Self-Regulated Learning Scale

2018 ◽  
Vol 34 (3) ◽  
pp. 193-205 ◽  
Author(s):  
Julia Steinbach ◽  
Heidrun Stoeger
Keyword(s):  
Factor Analysis ◽  
Primary School ◽  
Factor Structure ◽  
Concurrent Validity ◽  
Binary Logistic Regression ◽  
Affective Component ◽  
Self Regulated Learning ◽  
Regulated Learning ◽  
Teachers Attitudes ◽  
Development And Validation

Abstract. We describe the development and validation of an instrument for measuring the affective component of primary school teachers’ attitudes towards self-regulated learning. The questionnaire assesses the affective component towards those cognitive and metacognitive strategies that are especially effective in primary school. In a first study (n = 230), the factor structure was verified via an exploratory factor analysis. A confirmatory factor analysis with data from a second study (n = 400) indicated that the theoretical factor structure is appropriate. A comparison with four alternative models identified the theoretically derived factor structure as the most appropriate. Concurrent validity was demonstrated by correlations with a scale that measures the degree to which teachers create learning environments that enable students to self-regulate their learning. Retrospective validity was demonstrated by correlations with a scale that measures teachers’ experiences with self-regulated learning. In a third study (n = 47), the scale’s concurrent validity was tested with scales measuring teachers’ evaluation of the desirability of different aspects of self-regulated learning in class. Additionally, predictive validity was demonstrated via a binary logistic regression, with teachers attitudes as predictor on their registration for a workshop on self-regulated learning and their willingness to implement a seven-week training program on self-regulated learning.

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