Kategorie: Learning Analytics

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[publication] Learning Analytics and Spelling Acquisition in German-A First Prototype #IdERblog

At this year HCII conference we present a short overview about the IderBlog-project, titled „Learning Analytics and Spelling Acquisition in German-A First Prototype“

Abstract:

Data-driven learning in combination with emerging academic areas such as Learning Analytics (LA) has the potential to tailor students’ education to their needs [1]. The aim of this article is to present a web-based training platform for primary school pupils who struggle with the acquisition of German orthography. Our objective is the improvement in their writing and spelling competences. The focus of this article is on the development of the platform and the details concerning the requirements and the design of the User Interface (UI). In combination with Learning Analytics, the platform is expected to provide deeper insight into the process of spelling acquisition. Furthermore, aspects of Learning Analytics will help to develop the platform, to improve the exercises and to provide better materials in the long run.

[Draft version @ ResearchGate]

Reference: Ebner, M., Ebner, M., Edtstadler, K. (2016) Learning Analytics and Spelling Acquisition in German-A First Prototype. International Conference on Learning and Collaboration Technologies. pp. 405-416. Springer International Publishing

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[publication] Learning Analytics in MOOCs: Can Data Improve Students Retention and Learning? #LearningAnalytics

Our publication about „Learning Analytics in MOOCs: Can Data Improve Students Retention and Learning?“ at this year ED-Media conference is now online available. The presentation slides have been published right here.

Abstract:

In order to study learners’ behaviors and activities in online learning environments such as MOOCs, the demanding for a framework of practices and procedures to collect, analyze and optimize their data emerged in the educational learning horizon. Learning Analytics is the field that arose to comply with such needs and was denominated as a “technological fix to the long-standing problems” of online learning platforms (Knox, 2014). This paper discusses the significance of applying Learning Analytics in MOOCs to overcome some of its issues. We will mainly focus on improving students’ retention and learning using an algorithm prototype based on divergent MOOC indicators, and propose a scheme to reflect the results on MOOC students

[Full publication @ ResearchGate]

Reference: Khalil, M. & Ebner, M. (2016). Learning Analytics in MOOCs: Can Data Improve Students Retention and Learning?. In Proceedings of EdMedia: World Conference on Educational Media and Technology 2016 (pp. 569-576). Association for the Advancement of Computing in Education (AACE).

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[publication] Engaging Learning Analytics in MOOCS: the good, the bad, and the ugly #tugraz #mooc

Our publication about „Engaging Learning Analytics in MOOCS: the good, the bad, and the ugly“ at this year END Conference in Lubijana is now online available. The presentation slides have already been published here.

Abstract:

Learning Analytics is an emerging field in the vast areas of Educational Technology and Technology Enhanced Learning (TEL). It provides tools and techniques that offer researchers the ability to analyze, study, and benchmark institutions, learners and teachers as well as online learning environments such as MOOCs. Massive Open Online Courses (MOOCs) are considered to be a very active and an innovative form of bringing educational content to a broad community. Due to the reasons of being free and accessible to the public, MOOCs attracted a large number of heterogeneous learners who differ in education level, gender, and age. However, there are pressing demands to adjust the quality of the hosted courses, as well as controlling the high dropout ratio and the lack of interaction. With the help of Learning Analytics, it is possible to contain such issues. In this publication, we discuss the principles of engaging Learning Analytics in MOOCs learning environments and review its potential and capabilities (the good), constraints (the bad), and fallacy analytics (the ugly) based on our experience in last year’s.

[Full arcticle @ ResearchGate]

Reference: Khalil, M., Taraghi, B. & Ebner, M. (2016) Engaging Learning Analytics in MOOCS: the good, the bad, and the ugly. In:International Conference on Education and New Developments. Ljubljana, Slovenia, p. 3-7

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[publication] What Massive Open Online Course (MOOC) Stakeholders Can Learn from Learning Analytics? #tugraz

Our chapter about „What Massive Open Online Course (MOOC) Stakeholders Can Learn from Learning Analytics?“ got published as part of the International Compendium of Theory, Research, Practice, and Policy of Learning, Design and Technology.
Abstract:

Massive open online courses (MOOCs) are the road that led to a revolution and a new era of learning environments. Educational institutions have come under pressure to adopt new models that assure openness in their education distribution. Nonetheless, there is still altercation about the pedagogical approach and the absolute information delivery to the students. On the other side with the use of Learning Analytics, powerful tools become available which mainly aim to enhance learning and improve learners’ performance. In this chapter, the development phases of a Learning Analytics prototype and the experiment of integrating it into a MOOC platform, called iMooX will be presented. This chapter explores how MOOC stakeholders may benefit from Learning Analytics as well as it reports an exploratory analysis of some of the offered courses and demonstrates use cases as a typical evaluation of this prototype in order to discover hidden patterns, overture future proper decisions, and to optimize learning with applicable and convenient interventions.

[Full Chapter @ Springer]

[Draft Version @ ResearchGate]

Reference: Khalil, M., Ebner, M. (2016) What Massive Open Online Course (MOOC) Stakeholders Can Learn from Learning Analytics? In: Spector, M., Lockee, B., Childress, M. (Ed.), Learning, Design, and Technology: An International Compendium of Theory, Research, Practice, and Policy, Springer International Publishing, pp. 1-30

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[publication] De-Identification in Learning Analytics #LA #research

Our publication about „De-Identification in Learning Analytics“ got published in the Journal of Learning Analytics.
Abstract:

Learning Analytics has reserved its position as an important field in the educational sector. However, the large-scale collection, processing and analyzing of data have steered the wheel beyond the border lines and faced an abundance of ethical breaches and constraints. Revealing learners’ personal information and attitudes, as well as their activities, are major aspects that lead to personally identify individuals. Yet, de-identification can keep the process of Learning Analytics in progress while reducing the risk of inadvertent disclosure of learners’ identities. In this paper, the authors talk about de-identification methods in the context of learning environment and propose a first prototype conceptual approach that describes the combination of anonymization strategies and Learning Analytics techniques.

[Full Paper @ ResearchGate]

[Full Paper @ Journal’s Homepage]

Reference: Khalil, M. & Ebner, M. (2016) De-Identification in Learning Analytics. Journal of Learning Analytics. 3(1). pp. 129 – 138

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[publication] What is Learning Analytics about? A Survey of Different Methods Used in 2013-2015 #LearningAnalytics #tugraz

Our publication about „What is Learning Analytics about? A Survey of Different Methods Used in 2013-2015“ for this year Smart Learning Excellence Conference in Dubai is now online available. The slides have been already published here.
Abstract:

The area of Learning Analytics has developed enormously since the first International Conference on Learning Analytics and Knowledge (LAK) in 2011. It is a field that combines different disciplines such as computer science, statistics, psychology and pedagogy to achieve its intended objectives. The main goals illustrate in creating convenient interventions on learning as well as its environment and the final optimization about learning domain’s stakeholders (Khalil & Ebner, 2015b). Because the field matures and is now adapted in diverse educational settings, we believe there is a pressing need to list its own research methods and specify its objectives and dilemmas. This paper surveys publications from Learning Analytics and Knowledge conference from 2013 to 2015 and lists the significant research areas in this sphere. We consider the method profile and classify them into seven different categories with a brief description on each. Furthermore, we show the most cited method categories using Google scholar. Finally, the authors raise the challenges and constraints that affect its ethical approach through the meta-analysis study. It is believed that this paper will help researchers to identify the common methods used in Learning Analytics, and it will assist by establishing a future forecast towards new research work taking into account the privacy and ethical issues of this strongly emerged field.

[Full text @ ResearchGate]

Reference: Khalil, M., Ebner, M. (2016). What is Learning Analytics about? A Survey of Different Methods Used in 2013-2015. Conference proceeding of the 8th e-Learning Excellence Conference, 2016. Dubai, UAE. pp. 1-12

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[presentation] Learning Analytics in MOOCs: Can Data Improve Students Retention and Learning? #edmediaconf #tugraz

Our presentation at this year ED-Media Conference in Vancouver about „Learning Analytics in MOOCs: Can Data Improve Students Retention and Learning? “ is now online available. Here are the slides:

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[presentation] Bayesian Modelling of Student Misconceptions in the one-digit Multiplication with Probabilistic Programming #lak16 #research

Our presentation at this year conference on Learning Analytics (LAK 16) was about „Bayesian Modelling of Student Misconceptions in the one-digit Multiplication with Probabilistic Programming„. Here you can find the slides:

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[presentation] Engaging Learning Analytics in MOOCs: the good, the bad, and the ugly #tugraz #research

Mohammad is presenting our research work about „Engaging Learning Analytics in MOOCs: the good, the bad, and the ugly“ at this year END conference in Lubijana. Here are his presentation:

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[publication] Bayesian modelling of student misconceptions in the one-digit multiplication with probabilistic programming #lak16

Our contribution to this year Learning Analytics Conference was about „Bayesian modelling of student misconceptions in the one-digit multiplication with probabilistic programming„.
Abstract:

One-digit multiplication errors are one of the most extensively analysed mathematical problems. Research work primarily emphasises the use of statistics whereas learning analytics can go one step further and use machine learning techniques to model simple learning misconceptions. Probabilistic programming techniques ease the development of probabilistic graphical models (bayesian networks) and their use for prediction of student behaviour that can ultimately influence learning decision processes.

[Full paper @ ResearchGate]

[Full paper @ ACM Library]

Reference: Taraghi, B., Saranti, A., Legenstein, R. & Ebner, M. (2016) Bayesian modelling of student misconceptions in the one-digit multiplication with probabilistic programming. Proceedings of the Sixth International Conference on Learning Analytics & Knowledge, Edingburg, United Kingdom, 25/04/16 – 29/04/16, pp. 449-453., 10.1145/2883851.2883895