Evaluation Methods for Intelligent Tutoring Systems Revisited

Greer, Jim; Mark, Mary A.

doi:10.1007/s40593-015-0043-2

Cited by 41 publications

(26 citation statements)

References 8 publications

(6 reference statements)

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“…Learning analytics (LA) describes a set of various tools and approaches for handling large and complex student data and the contexts in which learning occurs (Greer & Mark, ). Although EDM preceded LA, the two research communities share a common goal of supporting education.…”

Section: Related Researchmentioning

confidence: 99%

Big Data and data science: A critical review of issues for educational research

Daniel

2017

Brit J Educational Tech

160

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Big Data refers to large and disparate volumes of data generated by people, applications and machines. It is gaining increasing attention from a variety of domains, including education. What are the challenges of engaging with Big Data research in education? This paper identifies a wide range of critical issues that researchers need to consider when working with Big Data in education. The issues identified include diversity in the conception and meaning of Big Data in education, ontological, epistemological disparity, technical challenges, ethics and privacy, digital divide and digital dividend, lack of expertise and academic development opportunities to prepare educational researchers to leverage opportunities afforded by Big Data. The goal of this paper is to raise awareness on these issues and initiate a dialogue. The paper was inspired partly by insights drawn from the literature but mostly informed by experience researching into Big Data in education.As a new research paradigm, Big Data in education stimulates new ways of framing research questions, designing studies, analyzing and visualizing data (Daniel, 2015;Dede et al., 2016). With the availability of large amounts of data in education, researchers can investigate subgroups within a population (a particular group of people), without necessarily relying on sophisticated probabilistic methods (Mayer-Sch€ onberger & Cukier, 2013). Further, Big Data tools enable researchers to collect large amounts of research data with relatively low cost (Mayer-Sch€ onberger, 2015).

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Section: Related Researchmentioning

confidence: 99%

Big Data and data science: A critical review of issues for educational research

Daniel

2017

Brit J Educational Tech

160

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“…Propensity-score matching is a type of nonrandomized study that can be used to minimize selection bias and estimate the effects of treatments on outcomes [14]. It works by matching students inside the treatment group with "doppelgangers" in a comparator group.…”

Section: Accounting For Individual Difference Using Propensity Score mentioning

confidence: 99%

Towards triggering higher-order thinking behaviors in MOOCs

Wang

Wen

Rosé

2016

Proceedings of the Sixth International Conference on Learning Analytics &Amp; Knowledge

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With the aim of better scaffolding discussion to improve learning in a MOOC context, this work investigates what kinds of discussion behaviors contribute to learning. We explored whether engaging in higher-order thinking behaviors results in more learning than paying general or focused attention to course materials. In order to evaluate whether to attribute the effect to engagement in the associated behaviors versus persistent characteristics of the students, we adopted two approaches. First, we used propensity score matching to pair students who exhibit a similar level of involvement in other course activities. Second, we explored individual variation in engagement in higher-order thinking behaviors across weeks. The results of both analyses support the attribution of the effect to the behavioral interpretation. A further analysis using LDA applied to course materials suggests that more social oriented topics triggered richer discussion than more biopsychology oriented topics.

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“…Application of analytics to educational data could help identify useful hidden patterns and trends [11]. Educational analytics refers to the process of extracting insight from large amount of educational data utilising the power of analytics broadly understood within Educational Data Mining, Academic Analytics, Learning Analytics and Teaching Analytics [2,35].…”

Section: Big Data and Analytics In Educational Researchmentioning

confidence: 99%

“…Demand for real-time or near real-time data collection, analysis and visualisation [10]. Greer and Mark [11], recommend the utilisation of visual techniques to identify valuable patterns in educational data that may not be apparent to many teachers working with ordinary statistical methods. Visualisation dashboards will help teachers with limited numerical knowledge to effortlessly understand and utilise teaching data [12,13].…”

Section: Introduction and Related Researchmentioning

confidence: 99%

Data Science Approach for Simulating Educational Data: Towards the Development of Teaching Outcome Model (TOM)

Ndukwe

Daniel

Butson

2018

BDCC

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Abstract:The increasing availability of educational data provides the educational researcher with numerous opportunities to use analytics to extract useful knowledge to enhance teaching and learning. While learning analytics focuses on the collection and analysis of data about students and their learning contexts, teaching analytics focuses on the analysis of the design of the teaching environment and the quality of learning activities provided to students. In this article, we propose a data science approach that incorporates the analysis and delivery of data-driven solution to explore the role of teaching analytics, without compromising issues of privacy, by creating pseudocode that simulates data to help develop test cases of teaching activities. The outcome of this approach is intended to inform the development of a teaching outcome model (TOM), that can be used to inspire and inspect quality of teaching. The simulated approach reported in the research was accomplished through Splunk. Splunk is a Big Data platform designed to collect and analyse high volumes of machine-generated data and render results on a dashboard in real-time. We present the results as a series of visual dashboards illustrating patterns, trends and results in teaching performance. Our research aims to contribute to the development of an educational data science approach to support the culture of data-informed decision making in higher education.

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Evaluation Methods for Intelligent Tutoring Systems Revisited

Cited by 41 publications

References 8 publications

Big Data and data science: A critical review of issues for educational research

Big Data and data science: A critical review of issues for educational research

Towards triggering higher-order thinking behaviors in MOOCs

Data Science Approach for Simulating Educational Data: Towards the Development of Teaching Outcome Model (TOM)

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