Scaffolding student teachers' information‐seeking behaviours with a network‐based tutoring system. (30th July 2019)

• Record Type: Journal Article
• Authors: Poitras, Eric; Mayne, Zachary; Huang, Lingyun; Udy, Laurel; Lajoie, Susanne
• Is Part Of: Journal of computer assisted learning. Volume 35:Number 6(2019)
• Page Start: 731
• Page End: 746
• Publication Date: 2019-07-30
• Subjects: network‐based tutors -- self‐regulated learning -- preservice teachers; Computer-assisted instruction -- Periodicals; 371.334
• DOI: 10.1111/jcal.12380
• Languages: English
• ISSNs: 0266-4909

Abstract

Abstract: Student teachers' instructional planning requires them to regulate certain aspects of their own learning while designing lessons. The aim of this study is to support student teachers' self‐regulated learning through the convergence effect, where network‐based tutors are designed to optimize system recommendations of online resources based on information‐seeking behaviours. A total of 68 student teachers were randomly assigned to either a dynamic or static version of nBrowser, which converged a network or not towards an optimal configuration. The structural equation model suggests that student teachers spent less time during the learning session using the dynamic version of nBrowser. Although student teachers were found to be more efficient in seeking and acquiring information and reported knowledge gains, they failed to perform better than those assigned to the static condition on the lesson plan design task. We discuss the implications for the convergence effect in the context of network‐based tutors. Lay Description: What is already known about this topic: Preservice teachers require resources to plan classroom activities, for example, open educational resources available on the web. Preservice teachers may lack the prerequisite skills to regulate their own learning, in particular, to seek and acquire information from online resources in an efficient manner. Computer‐based learning environments can be designed to support self‐regulated learning while navigating hypermedia. What this paper adds: Intelligent web browsers can leverage the linguistic features and information‐seeking behaviours of preservice teachers to personalize recommendations of online resources. Recommender systems stands to improve the efficiency of teachers' self‐regulated learning, in particular, their efforts to seek and acquire online resources. Implications for practice and/or policy: Efforts should be made to make artificial intelligence tools in education broadly available to teachers, including lesson planning tools that leverage open educational resources. Tools that support teachers in seeking and acquiring information may benefit their professional development by enabling them to allocate more attentional resources to other aspects of teaching.