Designing Technology-Enabled Instruction to Utilize Learning Analytics

Davies, Randall; Nyland, Rob; Bodily, Robert; Chapman, John H.; Jones, Brian; Young, Jay A.

doi:10.1007/s11528-016-0131-7

Cited by 19 publications

(18 citation statements)

References 16 publications

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“…Learning Analytics can obtain advantages of available educational datasets from different Learning Management System (LMS), Content Management System (CMS), Tutor-ing system and other learning proposed systems (Davies, et al 2017). Educational Institutions have been constantly producing and storing large amount of different kinds of students' data and use these data for different purposes like: preparing different kinds of reports for wide range of stakeholders, assessing teachers' and students' activities, make range of statistical processing.…”

Section: What Kinds Of Benefits Educational Data Can Bring To Learning Environments?mentioning

confidence: 99%

Learning Analytics - New Flavor and Benefits for Educational Environments

Klašnja-Milićević¹,

Ivanović²

2018

Informatics in Education

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Amount of educational data has been constantly increasing for years in all domains and kinds of education (formal or informal) and educational activities (teaching, learning, assessment, use of social media and collaboration and so on). Accordingly, Learning Analytics (LA) become a powerful mechanism for supporting learners, instructors, teachers, learning system designers and developers to better understand educational processes and predict learners' needs and performances. In this paper, we analyze the important dimensions and objectives of LA, application possibilities and some challenges to the beneficial exploitation of educational data. The required skills and capabilities that make meaningful use of LA techniques and technologies in this domain are considered and identified. Presented findings can act as a valuable guide for setting up LA services in support of educational practice. Also, they can be used as learner guidance, in quality assurance, curriculum development, and in improving learning process effectiveness and efficiency. Finally, this paper proposes the unavoidable constraints that affect LA technologies in education.

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Section: What Kinds Of Benefits Educational Data Can Bring To Learning Environments?mentioning

confidence: 99%

Learning Analytics - New Flavor and Benefits for Educational Environments

Klašnja-Milićević¹,

Ivanović²

2018

Informatics in Education

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“…The results of this exploratory study indicate that the use of student behaviors and text in a DL approach may improve the predictive power of identifying at-risk learners early enough in the learning process to allow for interventions that can change the course of their trajectory. However, part of the process in the development of predictive models includes a rigorous, intensive and iterative revision process (Davies et al , 2017). This process is especially relevant for exploratory studies such as this.…”

Section: Discussionmentioning

confidence: 99%

Improving predictive power through deep learning analysis of K-12 online student behaviors and discussion board content

Hung

Rice

Kepka

et al. 2020

IDD

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Purpose For studies in educational data mining or learning Analytics, the prediction of student’s performance or early warning is one of the most popular research topics. However, research gaps indicate a paucity of research using machine learning and deep learning (DL) models in predictive analytics that include both behaviors and text analysis. Design/methodology/approach This study combined behavioral data and discussion board content to construct early warning models with machine learning and DL algorithms. In total, 680 course sections, 12,869 students and 14,951,368 logs were collected from a K-12 virtual school in the USA. Three rounds of experiments were conducted to demonstrate the effectiveness of the proposed approach. Findings The DL model performed better than machine learning models and was able to capture 51% of at-risk students in the eighth week with 86.8% overall accuracy. The combination of behavioral and textual data further improved the model’s performance in both recall and accuracy rates. The total word count is a more general indicator than the textual content feature. Successful students showed more words in analytic, and at-risk students showed more words in authentic when text was imported into a linguistic function word analysis tool. The balanced threshold was 0.315, which can capture up to 59% of at-risk students. Originality/value The results of this exploratory study indicate that the use of student behaviors and text in a DL approach may improve the predictive power of identifying at-risk learners early enough in the learning process to allow for interventions that can change the course of their trajectory.

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“…With advances in technology and increases in technology-enabled instruction, researchers are able to gather considerably more information about the strategies students use to complete the learning activities required for a course [4]. Capturing data within the system allows researchers to analyze the temporal order of spontaneous individual activities of students as they complete a course [11].…”

Section: Previous Workmentioning

confidence: 99%

“…In contrast with previous data collection methods, one challenge of studying student learning strategies in technology-enabled instructional systems is the overwhelming amount of data available [4]. While we no longer need to rely completely on student selfreport to determine precisely what strategies students use when completing a course, deciding which data are important, capturing and linking all the relevant data, then creating actionable information from data can be a challenge.…”

Section: Introductionmentioning

confidence: 99%

Using Educational Data Mining to Identify and Analyze Student Learning Strategies

Allen¹,

Davies²,

Ball³

et al. 2019

Proceedings of the Annual Hawaii International Conference on System Sciences

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This paper explores student learning strategies in an introductory spreadsheets course. Student study habits were tracked at a level of detail not available in previous research. Detailed data were collected regarding reading, video watching, actions in practice assignments, references to assignment instructions, and actions in graded assignments. The analysis indicates that student strategies cluster into four primary learning groups. The study provides insight into how instructors can develop their courses and lectures in ways that better match the learning strategies of their students.

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Designing Technology-Enabled Instruction to Utilize Learning Analytics

Cited by 19 publications

References 16 publications

Learning Analytics - New Flavor and Benefits for Educational Environments

Learning Analytics - New Flavor and Benefits for Educational Environments

Improving predictive power through deep learning analysis of K-12 online student behaviors and discussion board content

Using Educational Data Mining to Identify and Analyze Student Learning Strategies

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