Forging new pathways of research and innovation in open and distance learning

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Learning Analytics in Distance Education: A Systematic Literature Review Selcan Kilis, Yasemin Gülbahar in the literature. This current study attempts to provide an overview of LA in distance education as a means to bridge the gap and reveal existing patterns. Methodology The purpose of this current study is to review the existing literature published on the subject area of learning analytics in distance education in order to reveal the current status of the field, and the methodologies applied as a means to guide further research. With this aim, this current study systematically reviewed the related literature using the phrase learning analytics in two leading databases, ERIC and Science Direct. Of the studies identified, only those with available full text were included in the current study. Duplicates and conference proceedings were then discarded; however, since there were many duplicates, the number was seen to decline remarkably. In accordance with the aim of this study, a set of keywords (see Table 1) were used in searching for existing research without the restriction of any time period. Results of these searches are presented in Table 1. Table 1: Results from Systematic Review of Studies in Learning Analytics in Distance Education Searched keywords Database ERIC Science Direct “learning analytics” 85 221 Journal articles 67 190 Full-text available 57 156 “learning analytics” & “online learning” 14 55 “learning analytics” & “online teaching” - 13 “learning analytics” & “online education” 10 24 “learning analytics” & “blended learning” 1 12 “learning analytics” & “blended teaching” - 2 “learning analytics” & “blended education” - 6 “learning analytics” & “distance learning” 8 11 “learning analytics” & “distance teaching” 6 4 “learning analytics” & “distance education” 7 12 “learning analytics” & “electronic learning” 9 1 “learning analytics” & “e-learning” 13 29 “learning analytics” & “open learning” 3 1 “learning analytics” & “open teaching” - - “learning analytics” & “open education” 1 - Selected studies were reviewed one by one, based on the chosen criteria of relevance with the settings of any form of distance education and research conducted based on empirical data. The selection process resulted in 25 empirical study journal articles being identified that related with any form of open distance education. These articles were then analysed based on qualitative content analysis. The results are explained in the next section. 312 Reaching from the roots – 9 th EDEN Research Workshop Proceedings, 2016, Oldenburg ISBN 978-615-5511-12-7
Learning Analytics in Distance Education: A Systematic Literature Review Selcan Kilis, Yasemin Gülbahar Results This study is a systematic review of related literature about learning analytics in distance education, specifically designed as a qualitative content analysis. Of the retrieved studies, 25 were included in the analysis depending on their relevance with distance education settings. The criteria to investigate retrieved studies included the learning setting, applied methodology, data sources, and data analysis. Findings from content analysis of the 25 studies are shown in Table 2 by author and year published. Table 2: A Systematic Review of Studies in Learning Analytics in Distance Education Author(s) & Publication Year Bainbridge, Melitski, & Zahradnik (2015) Cambruzzi, Rigo, & Barbosa (2015) Fidalgo-Blanco, Sein- Echaluce, García- Peñalvo, & Conde (2015) Goggins, Galyen, Petakovic, & Laffey (2016) Goggins, Xing, Chen, Chen, & Wadholm (2015) Hernández-García, González-González, Jiménez-Zarco, & Chaparro-Peláez (2015) Jo, Park, Yoon, & Sung (2016) Junco, & Clem (2015) Kagklis, Karatrantou, Tantoula, Panagiotakopoulos, & Verykios (2015) Kim, Park, Yoon, & Jo (2016) Laflen, & Smith (2016) Lonn, Aguilar, & Teasley (2015) Macfadyen, & Dawson (2012) Martin, & Whitmer (2016) Park, & Jo (2015) Park, Yu, & Jo, (2016) Prinsloo, Archer, Barnes, Chetty, & Van Zyl (2015) Rienties et al. (2016) Ruipérez-Valiente, Muñoz-Merino, Leony, & Kloos (2015) Participants Graduate students Undergraduate students Undergraduate students Undergraduate students Undergraduate students Undergraduate students Undergraduate students Undergraduate students Graduate students Undergraduate students Undergraduate students Undergraduate students Undergraduate students Undergraduate students Undergraduate students Undergraduate students Undergraduate students Undergraduate students Undergraduate students Applied Data Sources Data Analysis Methodology Quantitative Student log data Inferential statistics (logistic regression) Case study Experiment Qualitative analysis Quantitative Student log data Descriptive statistics, inferential statistics (correlation) Mixed method Interview, survey, ethnographic field notes, assignments Both qualitative and quantitative (network analytic), social network analysis Qualitative Student log, chat Observations, complex models such as treebased algorithms and neural networks Case study Student log data Social network analysis, visualisation, inferential statistics (correlation, ANOVA) Quantitative Student log data, survey Structural equation modelling Quantitative Student log data Descriptive statistics, inferential statistics (hierarchical multiple regression) Quantitative Discussion, student Descriptive statistics, text mining, social log data network analysis, sentiment analysis Quantitative Discussion, proxy variables, student log data Data mining techniques, random forest (RF) technique Quantitative Student log data Descriptive statics, inferential statistics Design based research Student log data, surveys Inferential statistics (paired sample t-test, multiple regression) Case study Student log data, Descriptive statics, inferential statistics observation, (correlation), visualisation techniques collective discussions, documents Quantitative Student log data, Descriptive statistics, inferential statistics interaction data (ANOVA, MANOVA, correlation) Case study Interview, survey Rapid prototyping, usability test, descriptive statistics Case study Student log data Data mining techniques (Latent Class Analysis method as a clustering approach), descriptive statistics Case study Student log data Qualitative analysis Quantitative Student log data Decision making, qualitative Quantitative Student log data Visualisation techniques Reaching from the roots – 9 th EDEN Research Workshop Proceedings, 2016, Oldenburg 313 ISBN 978-615-5511-12-7
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9 th EDEN Research Workshop Forging
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Introduction Distance education is
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TABLE OF CONTENTS ADVANCED DISTANCE
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LEARNING ANALYTICS Scaling Learning
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Forging new pathways of research an
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Towards a Framework for Virtual Int
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Towards Designing an Oxford Experie
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Emergent Evaluation: An Initial Exp
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A MOOC with a Difference: Creating
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Use of MOOCs in Traditional Classro
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Head Start Online: A MOOC for those
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Carrots and Sticks: Workload and Pe
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Online Learning in Community Colleg
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The Usability Evaluation of UniCamp
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Connecting in the Online Environmen
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Contextualising Learner Experience:
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Investigating the Effectiveness of
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WhatsApp: “Going where the Conver
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A Study of the Use of Ubiquitous Mo
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Differentiated Technology-Based Int
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Using Digital Technologies for Incl
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Video in Higher Education: Examinin
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Policy Review of Open Badges for Op
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Open Educational Resources (OER): G
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Monitoring Progress on Open Educati
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A Programmatic Approach to Blended
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Teaching Science through Technology
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Contributions to Teaching Practice
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The Irish Online Learning Landscape
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The State of Distance Education in
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Page 269 and 270: The State of Distance Education in
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Page 273 and 274: Scaling Learning Analytics: The Pra
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ARMAZEG Developing Tools for Lifelo
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LeHo Learning at Home and the Hospi
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SeaL Seamless Learning in Lake Cons
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Forging new pathways of research and innovation in open and distance learning

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