Using Data to Understand How to Better Design Adaptive Learning

Liu, Min; Kang, Jina; Wu, Zhongze; Lee, Hyeyeon; Pan, Zilong; Corliss, Stephanie B.

doi:10.1007/s10758-017-9326-z

Cited by 51 publications

(32 citation statements)

References 29 publications

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“…For further insights into students' responses to feedback, analyzing trace data seems to be a promising approach (Zimmerman 2008). For example, investigating students' behavioral patterns when dealing with learning materials, prompts or analyses of the learning analytics system related to their motivational dispositions might allow a higher adaptivity of learning analytics (Liu et al 2017).…”

Section: Discussionmentioning

confidence: 99%

“…To react accordingly and provide motivational interventions, learning analytics require information about the learners, their characteristics, and especially about their current motivational state, as well as the perceived relevance of the learning tasks (Keller 2008b;Liu et al 2017). Learning analytics may provide motivational interventions using data about learners, their behavior in the learning environment, and their interaction with the learning material.…”

Section: Motivation In Learning Analyticsmentioning

confidence: 99%

“…They use static and dynamic information about learners and learning environments, assessing, eliciting, and analyzing it, for real-time modeling prediction, and optimization of learning processes, learning environments, and educational decision-making (Ifenthaler 2015). Current research on learning analytics focusses on technical issues and data processing (Berland et al 2014;Costa et al 2017), on data privacy (Drachsler and Greller 2016;Ifenthaler and Schumacher 2016;Rubel and Jones 2016;West et al 2016), on developing user systems (d'Aquin et al 2014), on relationships between learner characteristics and learning outcome (Ellis et al 2017;Gašević et al 2017;Liu et al 2017), or on specific applications for dashboards (Park and Jo 2015;Schumacher and Ifenthaler 2018;Verbert et al 2013). However, linking learning analytics with learning theories is still at an early stage (Marzouk et al 2016).…”

Section: Introductionmentioning

confidence: 99%

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The importance of students’ motivational dispositions for designing learning analytics

Schumacher

2018

J Comput High Educ

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Depending on their motivational dispositions, students choose different learning strategies and vary in their persistence in reaching learning outcomes. As learning is more and more facilitated through technology, analytics approaches allow learning processes and environments to be analyzed and optimized. However, research on motivation and learning analytics is at an early stage. Thus, the purpose of this quantitative survey study is to investigate the relation between students' motivational dispositions and the support they perceive through learning analytics. Findings indicate that facets of students' goal orientations and academic self-concept impact students' expectations of the support from learning analytics. The findings emphasize the need to design highly personalized and adaptable learning analytics systems that consider students' dispositions and needs. The present study is a first attempt at linking empirical evidence, motivational theory, and learning analytics.

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Section: Discussionmentioning

confidence: 99%

Section: Motivation In Learning Analyticsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The importance of students’ motivational dispositions for designing learning analytics

Schumacher

2018

J Comput High Educ

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“…First, researchers utilized LA to evaluate the effectiveness of adaptive learning systems. For instance, Liu et al (2017) have utilized LA to understand pharmacy learners' usage patterns for learning adaptive system for their chemistry and biology modules and discovered that affective factor such as motivation can be indicative of student success, and further highlighted the importance of alignment between components within the system as well as the role of visualization of data using LA in understanding user behavior. Similarly, Mojarad, Essa, Mojarad, and Baker (2018) used LA techniques to evaluate the effectiveness of an adaptive learning system called ALEKS (Assessment and Learning in Knowledge Spaces).…”

Section: Role Of La To Support Personalized Learningmentioning

confidence: 99%

A conceptual framework for teaching computational thinking in personalized OERs

Moon

Lee

et al. 2020

Smart Learn. Environ.

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Interests towards teaching programming skills have risen recently in the realm of computing education. Learning how to program not only enables learners to develop computing applications, but it can also enhance learners' computational thinking (CT) practice. CT refers to learners' ability to approach ill-structured tasks systematically based on algorithmic thinking in computing. Along with growing academic interests towards CT in recent studies, researchers have emphasized the role of teaching programming in facilitating learners' problem-solving skills. Emerging OERs have expanded learners' opportunities to engage in hands-on programming exercises; yet a challenge still remains as to how learners' programming exercises can be tailored to accommodate individual differences in terms of learners' digital literacy skills. There is still a lack of in-depth discussions on how to support learners' personalized learning experiences during programming exercises associated with CT. This study hence proposes a conceptual framework that seeks to consider how to promotelearners' personalized learning experiences and enhance their CT skills in OERs. Through extensive reviewing of literature, this study provides several implications for further research.

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“…al. (2015),Park et al (2016),Ochoa et al (2018),Hutt et al (2017),Jayaprakash et al (2014),Gray et al (2016),Sandoval et al (2018),Giannakos et al (2019) Regression methodsLiu et al (2017), Di Mitri et al(2017), Mangaroska et al (2019), Sandoval et al (2018), Sun et al (2018), Giannakos et al (2019) Visualization Thompson et al (2014), Pardos and Kao (2015), Liu et al (2017), Mangaroska et al (2019), Wong et al (2018) Correlation Liu et al (2017), Raca et al (2016), Gray et al (2016), Mangaroska et al (2019) Descriptive statistics Raca et al (2016), Mangaroska et al (2019), Wong et al Samuelsen et al Research and Practice in Technology Enhanced Learning (2019) 14:11…”

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confidence: 99%

Integrating multiple data sources for learning analytics—review of literature

Samuelsen

Chen

Wasson

2019

RPTEL

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Learning analytics (LA) promises understanding and optimization of learning and learning environments. To enable richer insights regarding questions related to learning and education, LA solutions should be able to integrate data coming from many different data sources, which may be stored in different formats and have varying levels of structure. Data integration also plays a role for the scalability of LA, an important challenge in itself. The objective of this review is to assess the current state of LA in terms of data integration in the context of higher education. The initial search of six academic databases and common venues for publishing LA research resulted in 115 publications, out of which 20 were included in the final analysis. The results show that a few data sources (e.g., LMS) appear repeatedly in the research studies; the number of data sources used in LA studies in higher education tends to be limited; when data are integrated, similar data formats are often combined (a low-hanging fruit in terms of technical challenges); the research literature tends to lack details about data integration in the implemented systems; and, despite being a good starting point for data integration, educational data specifications (e.g., xAPI) seem to seldom be used. In addition, the results indicate a lack of stakeholder (e.g., teachers/instructors, technology vendors) involvement in the research studies. The review concludes by offering recommendations to address limitations and gaps in the research reported in the literature.

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Using Data to Understand How to Better Design Adaptive Learning

Cited by 51 publications

References 29 publications

The importance of students’ motivational dispositions for designing learning analytics

The importance of students’ motivational dispositions for designing learning analytics

A conceptual framework for teaching computational thinking in personalized OERs

Integrating multiple data sources for learning analytics—review of literature

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