What’s in a Step? Toward General, Abstract Representations of Tutoring System Log Data

VanLehn, Kurt; Koedinger, Kenneth R.; Skogsholm, Alida; Nwaigwe, Adaeze; Hausmann, Robert G. M.; Weinstein, Anders; Billings, Benjamin

doi:10.1007/978-3-540-73078-1_64

Cited by 10 publications

(6 citation statements)

References 2 publications

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“…Table 5 Least squared means and standard deviations (in parentheses) for representational effectiveness and representational efficiency at immediate posttest, delayed posttest by condition. Specifically, we examined "learning curves" using the DataShop web service (Anderson, 1993;Koedinger et al, 2010;VanLehn et al, 2007), which depict the average error rate (across students and knowledge components) as a function of the amount of prior practice (i.e., the number of opportunities a student has had to apply a given knowledge component). Following standard practice in Cognitive Tutors research, we viewed each step in a tutor problem as a learning opportunity for the particular knowledge component involved in the step.…”

Section: Analysis Of Tutor Logsmentioning

confidence: 99%

Interleaved practice in multi-dimensional learning tasks: Which dimension should we interleave?

Rau

Aleven

Rummel

2013

Learning and Instruction

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Section: Analysis Of Tutor Logsmentioning

confidence: 99%

Interleaved practice in multi-dimensional learning tasks: Which dimension should we interleave?

Rau

Aleven

Rummel

2013

Learning and Instruction

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“…Fine-grained data about SRL can be used by researchers in many ways, for example, to study development over time of students' SRL strategies or to study relations between SRL and domain-level learning in a more fine-grained manner than was previously possible. ITSs have long been capable of producing detailed, eventbased data regarding students' learning at the domain level (Corbett et al, 2000;Koedinger, Cunningham, Skogsholm, & Leber, 2008;VanLehn et al, 2007). With the addition of an automated method to assess SRL strategies in a finegrained event-based manner, an ITS can produce tightlycoupled streams of detailed data, one related to targeted SRL strategies, the other related to domain-level learning.…”

Section: Discussionmentioning

confidence: 99%

Automated, Unobtrusive, Action-by-Action Assessment of Self-Regulation During Learning With an Intelligent Tutoring System

Aleven

Roll

McLaren

et al. 2010

Educational Psychologist

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Assessment of students' self-regulated learning (SRL) requires a method for evaluating whether observed actions are appropriate acts of self-regulation in the specific learning context in which they occur. We review research that has resulted in an automated method for context-sensitive assessment of a specific SRL strategy, help seeking while working with an intelligent tutoring system. The method relies on a computer-executable model of the targeted SRL strategy. The method was validated by showing that it converges with other measures of help seeking. Automated feedback on help seeking driven by this method led to a lasting improvement in students' help-seeking behavior, although not in domain-specific learning. The method is unobtrusive, is temporally fine-grained, and can be applied on a large scale and over extended periods. The approach could be applied to other SRL strategies besides help seeking.

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“…Because CTRL is designed for adaptive collaborative learning systems rather than individual intelligent tutoring systems, the logging supported needs to be broader than the protocol discussed by VanLehn et al (2007). Thus, an additional type of message is supported: a scripting message, logged whenever a module changes the problem state of a tool.…”

Section: Component Integrationmentioning

confidence: 98%

CTRL: A research framework for providing adaptive collaborative learning support

Walker

Rummel

Koedinger

2009

User Model User-Adap Inter

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There is evidence suggesting that providing adaptive assistance to collaborative interactions might be a good way of improving the effectiveness of collaborative activities. In this paper, we introduce the Collaborative Tutoring Research Lab (CTRL), a research-oriented framework for adaptive collaborative learning support that enables researchers to combine different types of adaptive support, particularly by using domain-specific models as input to domain-general components in order to create more complex tutoring functionality. Additionally, the framework allows researchers to implement comparison conditions by making it easier to vary single factors of the adaptive intervention. We evaluated CTRL by designing adaptive and fixed support for a peer tutoring setting, and instantiating the framework using those two collaborative scenarios and an individual tutoring scenario. As part of the implementation, we integrated pre-existing components from the Cognitive Tutor Algebra (CTA) with custom-built components. The three conditions were then compared in a controlled classroom study, and the results helped us to contribute to learning sciences research in peer tutoring. CTRL can be generalized to other collaborative scenarios, but the ease of implementation relates to the complexity of the existing components used. CTRL as a framework has yielded a full implementation of an adaptive support system and a controlled evaluation in the classroom.

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What’s in a Step? Toward General, Abstract Representations of Tutoring System Log Data

Cited by 10 publications

References 2 publications

Interleaved practice in multi-dimensional learning tasks: Which dimension should we interleave?

Interleaved practice in multi-dimensional learning tasks: Which dimension should we interleave?

Automated, Unobtrusive, Action-by-Action Assessment of Self-Regulation During Learning With an Intelligent Tutoring System

CTRL: A research framework for providing adaptive collaborative learning support

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