Research Evidence on the Use of Learning Analytics-Implications for Education Policy

Technical Report · December 2016with86 Reads
DOI: 10.2791/955210
Report number: EUR 28294 EN
Abstract
Learning analytics is an emergent field of research that is growing fast. It takes advantage of the last decade of e-learning implementations n education and training as well as of research and development work in areas such as educational data mining, web analytics and statistics. In recent years, increasing numbers of digital tools for the education and training sectors have included learning analytics to some extent, and these tools are now in the early stages of adoption. This report reviews early uptake in the field, presenting five case studies and an inventory of tools, policies and practices. It also provides an Action List for policymakers, practitioners, researchers and industry members to guide work in Europe.
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    • teacher and student) demands for a robust and meaningful digital learning application. A recent policy paper from Joint Research Centre (JRC) showed for example that vendors delivering learning analytics, a great hype at the educational technology markets, do not focus enough resources to meet the needs of the end-users[2]. The overall picture at the educational technology markets seem confusing leaving academics, educational leaders, policy makers and especially teachers without answers about what improvements and benefit these technologies create for teachers and learners.
    [Show abstract] [Hide abstract] ABSTRACT: Teachers and educators are in need for understanding different technology and what is their added value for teaching and learning. This paper is to present an assessment framework that can identify such technology that delivers positive subjective outcomes for the end-users (teacher / learner). The framework consists of three main components: Capability Maturity Model (CMM), Pedagogical Usability and User Experience (UX). To validate the aforementioned approach the research group set up two focus groups. The first group consisted of developers of educational technology. The second group consisted of practitioners. Content analysis method was used to analyze recorded focus group data. Focus groups discussed about whether approach to conduct future assessment of educational technology was valid and suitable. Based on focus group results, the framework stands out as a valid foundation for a holistic assessment of edtech by better understanding the needs of different user stakeholders.
    Conference Paper · Sep 2017 · Assessment & Evaluation in Higher Education
    • Learning analytics is a growing field of research which deals with collecting and analyzing student data in order to understand and improve the learning process and the learning environments[4]. Prediction of student performance is one of the most popular goals, which aims to estimate future learning outcomes and identify indicators for learning success[10].
    [Show abstract] [Hide abstract] ABSTRACT: Predicting students’ performance is a popular objective of learning analytics, aimed at identifying indicators for learning success. Various data mining approaches have been applied for this purpose on student data collected from learning management systems or intelligent tutoring systems. However, the emerging social media-based learning environments have been less explored so far. Hence, in this paper we present an approach for predicting students’ performance based on their contributions on wiki, blog and microblogging tool. An innovative algorithm (Large Margin Nearest Neighbor Regression) is applied, and comparisons with other algorithms are conducted. Very good correlation coefficients are obtained, outperforming commonly used regression algorithms. Overall, results indicate that students’ active participation on social media tools is a good predictor of learning performance.
    Chapter · Jun 2017
  • [Show abstract] [Hide abstract] ABSTRACT: Feedback has been identified as one of the factors with the largest potential for a positive impact in a learning experience. There is a significant body of knowledge studying feedback and providing guidelines for its implementation in learning environments. In parallel, the areas of learning analytics or educational data mining have emerged to explore how to analyse exhaustive digital trails produced by technology mediation to improve learning experiences. Current conceptualisations of feedback do not take into account the presence of these trails nor the presence of knowledge extracted through analytics techniques. This paper presents a model to reconceptualise feedback in data-rich learning experiences. It acknowledges the presence of algorithms to analyse and predict learner behaviour and proposes an integrated view between these elements as part of the feedback process and aspects conventionally present in previous models. This new conceptualisation offers instructors, designers and researchers a framework to formulate feedback processes in scenarios with comprehensive data capturing, while retaining a solid connection with well-established educational theories.
    Article · Jul 2017
  • Chapter · Sep 2017 · Assessment & Evaluation in Higher Education
Project
The aim of the SharedOER study was to make an inventory of the existing cases within the context of formal education (focussing on later years in the school sector and early years of higher educat…" [more]
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