Closing the Experiential Learning Loops Using Learning Analytics Cycle: Towards Authentic Experience Sharing for Vocabulary Learning

Closing the Experiential Learning Loops Using Learning Analytics Cycle: Towards Authentic Experience Sharing for Vocabulary Learning

Mohammad Nehal Hasnine (Tokyo University of Agriculture and Technology, Fuchu, Japan), Hiroaki Ogata (Kyoto University, Kyoto, Japan), Gökhan Akçapınar (Hacettepe University, Ankara, Turkey), Kousuke Mouri (Tokyo University of Agriculture and Technology, Fuchu, Japan) and Keiichi Kaneko (Tokyo University of Agriculture and Technology, Fuchu, Japan)
Copyright: © 2020 |Pages: 21
DOI: 10.4018/IJDET.2020070105

Abstract

In ubiquitous learning, authentic experiences are captured and later reused as those are rich resources for foreign vocabulary development. This article presents an experiential theory-oriented approach to the design of learning analytics support for sharing and reusing authentic experiences. In this regard, first, a conceptual framework to support vocabulary learning using learners' authentic experiences is proposed. Next, learning experiences are captured using a context-aware ubiquitous learning system. Finally, grounded in the theoretical framework, the development of a web-based tool called learn from others (LFO) panel is presented. The LFO panel analyzes various learning logs (authentic, partially-authentic, and words) using the profiling method while determining the top-five learning partners inside a seamless learning analytics platform. This article contributes to the research in the area of theory-oriented design of learning analytics for vocabulary learning through authentic activities and focuses on closing the loops of experiential learning using learning analytics cycles.
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Introduction

Authentic Learning with Ubiquitous-Ness

Authentic learning describes real-life learning by applying knowledge in various real-life situations and contexts. Learning experiences in authentic contexts are considered to be a precious resource for learning, particularly in foreign language learning. For instance, Gilmore stated that authentic materials and authenticity in foreign language learning opposes contrived materials of traditional textbooks, which typically display a meager and frequently distorted sample of the target language while authentic materials offer a much richer source of input for learners (Gilmore, 2007). Duda and Tyne addressed authentic materials as the valuable resources for target language input (Duda & Tyne, 2010).

Some studies gently suggested using authentic learning materials as an alternative to traditional textbooks. For example, according to Tomlinson’s review study, various English language teaching materials, particularly global course-books currently make a significant contribution to the failure of many learners of English to acquire even basic competence in English and to the failure of most of them to develop the ability to use it successfully (Tomlinson, 2008). An authentic learning style inspires learners of new languages to create their authentic artifacts while traveling, studying overseas, working overseas, etc. that to be shared with their world, which is not possible in the traditional textbook-based learning approach.

Over the past years, we have observed the massive development of sensing technologies (e.g., smart glass, lifelong camera, multi-touch interface, wearable smart tracker, and bluetooth) (Hasnine et al., 2018). These technologies made it possible to capture contextual information such as people, date, precise time, location, and theme regarding the learners’ usage of various ubiquitous technologies. By using such technologies, learners' authentic learning experiences can be tracked and recorded quickly. For instance, an international student, upon experiencing a culturally authentic content, records it in the system with its context information (memo), picture/video/voice-data, together with its textual information. Ubiquitous functionalities automatically track the learning location, time, and place (Hasnine et al., 2019).

In this way, a vast amount of rich educational big data on authentic learning experiences can be captured. Now the questions arise, a) how this vast amount of educational data can be used to improve next-generation education? Also, b) can learning analytics provide solutions to sharing and reusing those captured authentic learning experiences (i.e., logs) among a community of language learners having similar learning interests in the right way at the right time and place?

A learning theory-oriented approach may solve the problem by playing an impact on the learning process; however, it depends on many factors, including timing, quality of feedback, interaction level with the feedback tool and many more.

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