As the fields of learning analytics and learning design mature, the convergence and synergies between the two are becoming an important area for research This paper intends to summarize the main outcomes of a systematic review of empirical evidence on learning analytics for learning design Moreover, this paper presents an overview of what and how learning analytics have been used to inform learning design decisions and in what contexts The search was performed in seven academic databases, resulting in 43 papers included in the main analysis The results from the review depict the ongoing design patterns and learning phenomena that emerged from the synergy that learning analytics and learning design impose on the current status of learning technologies Finally, this review stresses that future research should consider developing a framework on how to capture and systematize learning design data grounded in learning analytics and learning theory, and document what learning design choices made by educators influence subsequent learning activities and performances over time

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Learning Analytics for Learning Design: A Systematic Literature Review of Analytics-Driven Design to Enhance Learning

ALAS-KA is a tool that extends the learning analytics features of the Khan Academy platform.ALAS-KA includes new visualizations for the entire class and individual students of more than 20 new learning indicators.ALAS-KA helps teachers to make decision supported by the high level information provided.ALAS-KA enables students to gain awareness of their learning process for self-reflection.ALAS-KA can be used by the course instructors to detect class tendencies and learner models. The Khan Academy platform enables powerful on-line courses in which students can watch videos, solve exercises, or earn badges. This platform provides an advanced learning analytics module with useful visualizations. Nevertheless, it can be improved. In this paper, we describe ALAS-KA, which provides an extension of the learning analytics support for the Khan Academy platform. We herein present an overview of the architecture of ALAS-KA. In addition, we report the different types of visualizations and information provided by ALAS-KA, which have not been available previously in the Khan Academy platform. ALAS-KA includes new visualizations for the entire class and also for individual students. Individual visualizations can be used to check on the learning styles of students based on all the indicators available. ALAS-KA visualizations help teachers and students to make decisions in the learning process. The paper presents some guidelines and examples to help teachers make these decisions based on data from undergraduate courses, where ALAS-KA was installed. These courses (physics, chemistry, and mathematics) for freshmen were developed at Universidad Carlos III de Madrid (UC3M) and were taken by more than 300 students.

Alas-ka

This article proposes a framework of quality indicators for learning analytics that aims to standardise the evaluation of learning analytics tools and to provide a mean to capture evidence for the impact of learning analytics on educational practices in a standardised manner. The criteria of the framework and its quality indicators are based on the results of a Group Concept Mapping study conducted with experts from the field of learning analytics. The outcomes of this study are further extended with findings from a focused literature review.

https://www.jstor.org/stable/jeductechsoci.17.4.117

Quality Indicators for Learning Analytics.

From the conceptualization to the evaluation of computer-supported collaborative learning (CSCL) scenarios, teachers address multiple tasks, sometimes being overwhelmed on account of the required time and associated burden. To support teachers in this endeavor, we propose to connect the pedagogical decisions made at design time with the analysis of the participants' interactions. Thus, teachers would be provided with relevant and coarse-grained information that could help them manage their CSCL scenarios. This paper synthesizes the main contributions obtained from a 3-year design-based research process, and presents the findings obtained from the evaluation of the current proposal in two authentic CSCL scenarios. The participant teachers valued the proposal positively and stated that it was helpful for their orchestration of CSCL scenarios.

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Scripting and monitoring meet each other: Aligning learning analytics and learning design to support teachers in orchestrating CSCL situations

In the past few years, there has been an increasing interest in Massive Open Online Courses (MOOCs) as a new form of Technology-Enhanced Learning (TEL), in higher education and beyond. Recognizing the limitations of standalone MOOCs, blended MOOCs (bMOOCs) that aim at bringing in-class (i.e. face-to-face) interactions and online learning components together have emerged as an alternative MOOC model of teaching and learning in a higher education context. In this paper, we present the design, implementation, and evaluation details of a bMOOC course on “Teaching Methodologies” at Fayoum University, Egypt in cooperation with RWTH Aachen University, Germany, provided using the bMOOC platform L2P-bMOOC. In order to gauge the usability and effectiveness of the course, we employed an evaluation approach based on Conole’s 12 dimensions rubrics, ISONORM 9241/110-S as a general usability evaluation, and a custom effectiveness questionnaire reflecting the different MOOC stakeholder perspectives.

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A Usability Evaluation of a Blended MOOC Environment: An Experimental Case Study.

Learning analytics tools should be useful, i.e., they should be usable and provide the functionality for reaching the goals attributed to learning analytics. This paper seeks to unite learning analytics and action research. Based on this, we investigate how the multitude of questions that arise during technology-enhanced teaching and learning systematically can be mapped to sets of indicators. We examine, which questions are not yet supported and propose concepts of indicators that have a high potential of positively influencing teachers' didactical considerations. Our investigation shows that many questions of teachers cannot be answered with currently available research tools. Furthermore, few learning analytics studies report about measuring impact. We describe which effects learning analytics should have on teaching and discuss how this could be evaluated.

Supporting action research with learning analytics

eleed, Iss. 10 - In recent years, learning analytics (LA) has attracted a great deal of attention in technology-enhanced learning (TEL) research as practitioners, institutions, and researchers are increasingly seeing the potential that LA has to shape the future TEL landscape. Generally, LA deals with the development of methods that harness educational data sets to support the learning process. This paper provides a foundation for future research in LA. It provides a systematic overview on this emerging field and its key concepts through a reference model for LA based on four dimensions, namely data, environments, context (what?), stakeholders (who?), objectives (why?), and methods (how?). It further identifies various challenges and research opportunities in the area of LA in relation to each dimension.

Learning Analytics: Challenges and Future Research Directions

There are many different ways and models how to characterize usage data to enable representation of user actions across learning management system, and systems in general. Based on this data, learning analytics can perform different analysis and provide personalized and meaningful information to improve the learning and teaching processes. There is a variety of usage data formats that are already successfully used in exiting systems. These different usage data formats have their advantages and disadvantage that have to be considered when using them in the context of learning analytics. In this paper, several usage data formats are presented and analyzed in the context of learning analytics to help in choosing the best suiting usage data model.

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Data Models in Learning Analytics

Learning analytics tools should be useful, i.e., they should be usable and provide the functionality for reaching the goals attributed to learning analytics. We present a short summary of the learning analytics goals, and the importance of evaluation of learning analytics while trying to attain these goals. Furthermore, we present three different case studies of learning analytics eval- uation, and in the end provide a short outlook about the necessity of systematic way of learning analytics evaluation.

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Learning Analytics Evaluation - Beyond Usability.

In this paper, we present the fundamental concepts, design, and preliminary findings of a personalized gamification project in the educational context. The project aims to create a balanced, gamified learning environment in which all learners are equally engaged and interested. However, finding the balance between heterogenic learners’ traits and the variety of gamification design elements is a challenging, multistep process. Therefore, this paper presents the first steps towards the design of a balanced gamified environment, in which we a) proposed the Personalized Gamification Design Model (PeGaM) to assist gamification designers in applying a learner-center approach to gamification; b) explored learners’ learning tendencies as personalization criteria and applied PeGaM in the e-learning course, and c) conducted the exploratory study with 124 students to investigate behavioral differences in and between students in the control and treatment group. The study revealed a positive influence of badges, leaderboards, and experience points on learners with reflective, global, visual, and intuitive learning tendencies, and negative on students with sensing learning tendencies. Hence, this study supported the idea of introducing learning tendencies in the gamification design process and indicates the need for further research in this direction.

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Vlatko Lukarov

Papers

Supporting action research with learning analytics

Learning Analytics: Challenges and Future Research Directions

Data Models in Learning Analytics

Learning Analytics Evaluation - Beyond Usability.

A Fundamental Study for Gamification Design : Exploring Learning Tendencies' Effects