2020, Volume 23, Issue 4

Special Issue on "Best Practices and Guidelines for Conducting Online Synchronous Conferences"

Guest Editor(s): Nian-Shing Chen

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Full Length Articles

Hsu-Wen Huang

Department of Linguistics and Translation, City University of Hong Kong, Hong Kong // hwhuang@cityu.edu.hk

Jung-Tai King

Institute of Neuroscience, National Yang-Ming University, Taiwan // pax2@nctu.edu.tw

Chia-Lin Lee

Graduate Institute of Linguistics, National Taiwan University, Taiwan // Department of Psychology, National Taiwan University, Taiwan // Graduate Institute of Brain and Mind Sciences, National Taiwan University, Taiwan // Neurobiology and Cognitive Neuroscience Center, National Taiwan University, Taiwan // chialinlee@ntu.edu.tw


ABSTRACT:

Integrating education practices and measurements of brain activity has the potential to make learning more engaging and productive. Direct recordings of electrical activity in the brain provide important information about the complex dynamics of the cognitive processes and mental states that occur during learning, which can ultimately empower learners. In this article, electroencephalographic (EEG) methodologies, including the time-frequency and event-related potential techniques, are introduced, and the application of these techniques to studies of digital learning studies is discussed. Considerations of how to collect high quality data in both laboratory and real world settings are also presented, along with potential research directions. Finally, a general guideline for publishing results is offered. These issues are critical for producing useful applications of EEG studies to the digital learning research community.

Keywords:

Digital learning, Electroencephalograph (EEG), Event-related potentials, Dry-wireless EEG

Hiroyuki Kuromiya

Graduate School of Informatics, Kyoto University, Japan // khiroyuki1993@gmail.com

Rwitajit Majumdar

Academic Center for Computing and Media Studies, Kyoto University, Japan // dr.rwito@gmail.com

Hiroaki Ogata

Academic Center for Computing and Media Studies, Kyoto University, Japan // hiroaki.ogata@gmail.com


ABSTRACT:

Evidence-based education has become more relevant in the current technology-enhanced teaching-learning era. This paper introduces how Educational BIG data has the potential to generate such evidence. As evidence-based education traditionally hooks on the meta-analysis of the literature, so there are existing platforms that support manual input of evidence as structured information. However, such platforms often focus on researchers as end-users and its design is not aligned to the practitioners’ workflow. In our work, we propose a technology-mediated process of capturing teaching-learning cases (TLCs) using a learning analytics framework. Each case is primarily a single data point regarding the result of an intervention and multiple such cases would generate an evidence of intervention effectiveness. To capture TLCs in our current context, our system automatically conducts statistical modelling of learning logs captured from Learning Management Systems (LMS) and an e-book reader. Indicators from those learning logs are evaluated by the Linear Mixed Effects model to compute whether an intervention had a positive learning effect. We present two case studies to illustrate our approach of extracting case effectiveness from two different learning contexts – one at a junior-high math class where email messages were sent as intervention and another in a blended learning context in a higher education physics class where an active learning strategy was implemented. Our novelty lies in the proposed automated approach of data aggregation, analysis, and case storing using a Learning Analytics framework for supporting evidence-based practice more accessible for practitioners.

Keywords:

Learning analytics, Evidence-based education, Technology-enhanced Evidence-based Education & Learning (TEEL), Learning Evidence Analytics Framework (LEAF), Mixed effects model, Teaching-learning case

How Gender Pairings Affect Collaborative Problem Solving in Social- Learning Context: The Effects on Performance, Behaviors, and Attitudes

Yu-Tzu Lin, Cheng-Chih Wu, Zhi-Hong Chen, and Pei-Yi Ku

Effects of Mindful Learning using a Smartphone Lens in Everyday Life and Attitude toward Mobile-based Learning on Creativity Enhancement

Yu-Chu Yeh, Chih-Yen Chang, Yu-Shan Ting and Szu-Yu Chen

Review of Studies on Recognition Technologies and Their Applications Used to Assist Learning and Instruction

Rustam Shadiev, Zi Heng Zhang, Ting-Ting Wu and Yueh Min Huang

Analyzing Contextual levels and Applications of Technological Pedagogical Content Knowledge (TPACK) in English as a Second Language Subject Area: A Systematic Literature Review

Moe D. Greene and William M. Jones

The Effects of Adopting Tablets and Facebook for Learning Badminton Skills: A Portfolio-based WISER Model in Physical Education

Kuo-Chin Lin, I-Chen Lee, Chih-Fu Cheng and Hui-Chun Hung

Editorial

Best Practices and Guidelines for Conducting Online Synchronous Conferences

Nian-Shing Chen

Special Issue Articles


Starting from Volume 17 Issue 4, all published articles of the journal of Educational Technology & Society are available under Creative Commons CC-BY-ND-NC 3.0 license.