ERIC Number: EJ1257783
Record Type: Journal
Publication Date: 2015
Pages: 4
Abstractor: ERIC
ISBN: N/A
ISSN: ISSN-1540-0182
EISSN: N/A
Towards Data-Driven Instruction
Hershkovitz, Arnon
Technology, Instruction, Cognition and Learning, v10 n1 p1-4 2015
Data-driven instruction is still a huge scope and has many shades. One promising way of adding learning analytics to traditional teaching is to offer teachers accessible, data-driven information, either in a dashboard style or with a UI with which they could perform their own analysis on student data (e.g., Ben-Naim, Bain, & Marcus, 2009; Dyckhoff, Zielke, Bültman, Chatti, Schroeder, 2012; Murray et al., 2013; Pedraza-Perez, Romero & Ventura, 2011; Verbert, Duval, Klerkx, Govaertz, & Santos, 2013). Similarly, students may also be able to reflect on their own learning, based on data-driven mechanisms, which might facilitate reflection on their own learning, increase self-regulation and eventually promote learner-centered instruction (e.g., May, George, & Prévôt, 2011; Rivera-Pelayo, Munk, Zacharias, & Braun, 2013). Other common methods of improving instructional processes using analytics are supplying students with immediate feedback and assisting teachers with automatic assessment. These kinds of approaches to improving instructional processes are discussed in this special issue. Overall, the first issue of this double special issue gives a broad picture of the state-of-the-art in the field, demonstrating how instruction in various domains and in various grade levels can be assisted with analytics. Improving instruction might be a crucial step towards improving learning.
Descriptors: Data Use, Learning Analytics, Computer Interfaces, Reflection, Self Management, Feedback (Response), Instructional Improvement, Educational Research, Formative Evaluation, Users (Information)
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Publication Type: Journal Articles; Reports - Descriptive
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Grant or Contract Numbers: N/A