Learning Analytics: From Theory to Practice – Data Support for Learning and Teaching

Greller, Wolfgang; Ebner, Martin; Schön, Martin

doi:10.1007/978-3-319-08657-6_8

Cited by 27 publications

(22 citation statements)

References 6 publications

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“…The institution can use this information in a proactive approach to intervene at an early stage of risk, and create and adapt appropriate support services in order to enhance teaching quality, students' first-year experience, and thus student retention in higher education (Arnold and Pistilli 2012;Colvin et al 2015;Gaševic et al 2016). For educators, learning analytics provides real-time insight into students' performance and progress (Corrin et al 2013) and therefore the opportunity to refine their practice, plan teaching activities, and create a learning environment that is highly adaptive for students as well as to intervene early enough by providing appropriate support to improve students' chances of success and prevent them from failing a course (Arnold and Pistilli 2012;Barber and Sharkey 2012;Greller et al 2014). Hence, teachers need to be competent in interpreting the data (Papamitsiou and Economides 2014;Romero et al 2008).…”

Section: Learning Analytics In Higher Educationmentioning

confidence: 98%

Learning Analytics and Digital Badges: Potential Impact on Student Retention in Higher Education

Mah

2016

Tech Know Learn

109

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Learning analytics and digital badges are emerging research fields in educational science. They both show promise for enhancing student retention in higher education, where withdrawals prior to degree completion remain at about 30 % in Organisation for Economic Cooperation and Development member countries. This integrative review provides an overview of the theoretical literature as well as current practices and experience with learning analytics and digital badges in higher education with regard to their potential impact on student retention to enhance students' first-year experience. Learning analytics involves measuring and analyzing dynamic student data in order to gain insight into students' learning processes and optimize learning and teaching. One purpose of learning analytics is to construct predictive models to identify students who risk failing a course and thus are more likely to drop out of higher education. Personalized feedback provides students with information about academic support services, helping them to improve their skills and therefore be successful in higher education. Digital badges are symbols for certifying knowledge, skills, and competencies on web-based platforms. The intention is to encourage student persistence by motivating them, recognizing their generic skills, signaling their achievements, and capturing their learning paths. This article proposes a model that synthesizes learning analytics, digital badges, and generic skills such as academic competencies. The main idea is that generic skills can be represented as digital badges, which can be used for learning analytics algorithms to predict student success and to provide students with personalized feedback for improvement. Moreover, this model may serve as a platform for discussion and further research on learning analytics and digital badges to increase student retention in higher education.

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Section: Learning Analytics In Higher Educationmentioning

confidence: 98%

Learning Analytics and Digital Badges: Potential Impact on Student Retention in Higher Education

Mah

2016

Tech Know Learn

109

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“…• Counts: Some of the systems monitored data in terms of numbers. In his paper, Duval (2011) collected the number of logins and assignments finished while Holman et al (2013) also collected the number of content views and Greller et al (2014) the number of questions answered.…”

Section: Different Types Of Data Collectedmentioning

confidence: 99%

“…• Game interactions / actions: This type of data gives an insight into the player's actual interactions with the game. It can be very general, such as a player's state in a game or more specific such as clicks or answers given to a question (Greller et al, 2014;Martin et al, 2013;. Piech et al (2012) even describe how they logged snapshots of code whenever a program was saved or compiled.…”

Section: Different Types Of Data Collectedmentioning

confidence: 99%

Specification and evaluation of an assessment engine for educational games: Empowering educators with an assessment editor and a learning analytics dashboard

Chaudy

Connolly

2018

Entertainment Computing

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Assessment is a crucial aspect of any teaching and learning process. Educational games offer promising advantages for assessment; personalised feedback to students and automated assessment process. However, while many teachers agree that educational games increase motivation, learning and retention, few of them are ready to fully trust them as an assessment tool. We believe there are two main reasons for this lack of trust: educators are not given sufficient information about the gameplays, and many educational games are distributed as black-boxes, unmodifiable by teachers. This paper presents an assessment engine designed to separate a game and its assessment. It allows teachers to modify a game's assessment after distribution and visualise gameplay data via a learning analytics dashboard. The engine was evaluated quantitatively by 31 educators. Findings were overall very positive: both the assessment editor and the learning analytics dashboard were rated useful and easy to use. The evaluation also indicates that, having access to EngAGe, educators would be more likely to trust a game's assessment. This paper concludes that EngAGe can be used by educators effectively to modify educational games' assessment and visualise gameplay data, and that it contributes to increasing their trust in educational games as an assessment tool.

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“…Interactions within platforms can be captured for later analysis to gain a deeper understanding of the learning process (Khalil and Ebner, 2015). LA allows to use these information to perceive learning issues and makes it possible for teachers to actively intervene (Siemens and Long, 2011;Greller and Drachsler, 2012;Greller et al, 2014). Appropriate visualizations can be used to provide insights for teachers, students, parents and academic personnel as well (Ruiprez-Valiente et al, 2015).…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Learning analytics to improve writing skills for young children – an holistic approach

Steinhauer

Gros

Ebner

et al. 2017

JRIT&L

Self Cite

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Purpose Due to the important role of orthography in society, the project called IDeRBlog presented in this paper created a web-based tool to motivate pupils to write text as well as to read and to comment on texts written by fellow students. In addition, IDeRBlog aims to improve student’s German orthography skills and supports teachers and parents with training materials for their students. The paper aims to discuss these issues. Design/methodology/approach With the aid of learning analytics, the submitted text is analyzed and special feedback is given to the students so that they can try to correct the misspelled words themselves. The teachers as well as the parents are benefiting from the analysis and exercises suggested by the system. Findings A recent study showed the efficiency of the system in form of an improvement of the students’ orthographic skills. Over a period of four months 70 percent of the students achieved a significant reduction of their spelling mistakes. Originality/value IDeRBlog is an innovative approach to improving orthography skills combining blogging and new media with writing and practice.

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Learning Analytics: From Theory to Practice – Data Support for Learning and Teaching

Cited by 27 publications

References 6 publications

Learning Analytics and Digital Badges: Potential Impact on Student Retention in Higher Education

Learning Analytics and Digital Badges: Potential Impact on Student Retention in Higher Education

Specification and evaluation of an assessment engine for educational games: Empowering educators with an assessment editor and a learning analytics dashboard

Learning analytics to improve writing skills for young children – an holistic approach

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