Using Data-Driven Discovery of Better Student Models to Improve Student Learning

Koedinger, Kenneth R.; Stamper, John C.; McLaughlin, Elizabeth A.; Nixon, Tristan

doi:10.1007/978-3-642-39112-5_43

Cited by 62 publications

(43 citation statements)

References 7 publications

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“…However, it has not always been in line with particulars of the research design (e.g., Carnegie Learning added an upfront example in each unit, but the recommended approximate 50-50 example-problem ratio and use of fading were not incorporated). Some of the most recent and powerful demonstrations have shown how learning is improved through inserting new carefully designed tasks based on data analytic approaches that discover hidden skills that were not addressed in prior instruction (Koedinger and McLaughlin 2010;Koedinger et al 2013). Neither of those particular results nor the methodology for producing them have been incorporated in industry.…”

Section: Q) What Was Particularly Challenging In Undertaking This Study?mentioning

confidence: 99%

An Interview Reflection on “Intelligent Tutoring Goes to School in the Big City”

Koedinger

Aleven

2015

Int J Artif Intell Educ

Self Cite

209

198

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Our 1997 article in IJAIED reported on a study that showed that a new algebra curriculum with an embedded intelligent tutoring system (the Algebra Cognitive Tutor) dramatically enhanced high-school students' learning. The main motivation for the study was to demonstrate that intelligent tutors that have cognitive science research embedded in them could have real impact in schools. This study was one of the first large-scale classroom evaluations of the integrated use of an Intelligent Tutoring System (ITS) in high schools. A core challenge was figuring out how to embed this new technology into a curriculum and into the existing social context of schools. A key element of the study design was to include multiple kinds of assessments, including standardized test items and items measuring complex problem solving and use of representations. The results were powerful: BOn average the 470 students in experimental classes outperformed students in comparison classes by 15 % on standardized tests and 100 % on tests targeting the [course] objectives.^We suggested that the study was evidence Bthat laboratory tutoring systems can be scaled up and made to work, both technically and pedagogically, in real and unforgiving settings like urban high schools.^Since this study, many more classroom studies comparing instruction that includes an ITS against business as usual have been conducted, often showing advantages for the ITS-enhanced curricula. More rigorous randomized field trials are now more commonplace, but the approach of using multiple assessments in large-scale randomized field trials has not caught on. Cognitive task analysis will remain fundamental to the success of ITSs. A key remaining question for ITS is to find out how they can be used most effectively to support open-ended problem solving, either online or offline. Given all the recent excitement around Massive Open Online Courses (MOOCs), it is interesting to note that our field of

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Section: Q) What Was Particularly Challenging In Undertaking This Study?mentioning

confidence: 99%

An Interview Reflection on “Intelligent Tutoring Goes to School in the Big City”

Koedinger

Aleven

2015

Int J Artif Intell Educ

Self Cite

209

198

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“…Stamper and Koedinger used a learning curve analysis of geometry tutor data to discover a hidden planning skill on problems that cannot be solved by simply applying a single formula [12]. Koedinger, Stamper, McLaughlin, and Nixon redesigned the tutor based on this discovery and compared it with the prior tutor in an in vivo experiment [13]. Students using the redesigned tutor reached tutor--determined mastery in 25 percent less time and did better on a paper post--test, especially on difficult problems requiring the hidden planning skill that was discovered.…”

Section: Cognitive Task Analysis Based On Quantitative Analysis Of Edmentioning

confidence: 99%

“…Nevertheless, such models have been usefully interpreted to suggest modifications to improve educational materials. Randomized controlled experiments have demonstrated that such modifications can yield reliable and substantial improvements in student learning efficiency and post--instruction effectiveness [13].…”

Section: Opportunities For Improving Moocsmentioning

confidence: 99%

Data-driven Learner Modeling to Understand and Improve Online Learning

2014

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Editor's IntroductionAdvanced educational technologies are developing rapidly and online MOOC courses are becoming more prevalent, creating an enthusiasm for the seemingly limitless data-driven possibilities to affect advances in learning and enhance the learning experience. For these possibilities to unfold, the expertise and collaboration of many specialists will be necessary to improve data collection, to foster the development of better predictive models, and to assure models are interpretable and actionable. The big data collected from MOOCs needs to be bigger, not in its height (number of students) but in its width-more meta-data and information on learners' cognitive and self-regulatory states needs to be collected in addition to correctness and completion rates. This more detailed articulation will help open up the black box approach to machine learning models where prediction is the primary goal. Instead, a data-driven learner model approach uses fine grain data that is conceived and developed from cognitive principles to build explanatory models with practical implications to improve student learning.

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“…Hence, the help and guidance provided, for example, learning resource and strategy recommendation, is not fully targeted and efficient. In order to cover the shortage of the models above, a few ones which pay more attention to learning activities and learners' data are proposed [14] [15]. Based on the data analysis, a better student model and more accurate evaluation of per student are achieved.…”

Section: Introductionmentioning

confidence: 99%

A Personalized User Evaluation Model for Web-Based Learning Systems

Niu

Zhao

et al. 2014

2014 IEEE 26th International Conference on Tools With Artificial Intelligence

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With the development of computer science and multimedia technology, Web-based learning becomes increasingly popular. User evaluation plays a significant role in the process of guided learning. In recent years, there is great progress in the development of evaluation technology. However, few evaluation methods fully take online learning activity analysis and individual differences into account. This paper proposes a personalized user evaluation model for Webbased learning systems. The model is utilized to record and analyze various learning activities throughout the entire learning process. Considering individual differences, learners are clustered and specific evaluation standards are set for different clusters. Comprehensive evaluation is achieved by combining Analytic Hierarchy Process, Fuzzy C-Means clustering and normalization algorithm. Through the comparison with several other common evaluation methods, experimental results show that the proposed method outperforms existing ones on the accuracy of learner evaluation.

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Using Data-Driven Discovery of Better Student Models to Improve Student Learning

Cited by 62 publications

References 7 publications

An Interview Reflection on “Intelligent Tutoring Goes to School in the Big City”

An Interview Reflection on “Intelligent Tutoring Goes to School in the Big City”

Data-driven Learner Modeling to Understand and Improve Online Learning

A Personalized User Evaluation Model for Web-Based Learning Systems

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