Help Seeking and Intelligent Tutoring Systems: Theoretical Perspectives and a Step Towards Theoretical Integration

Aleven, Vincent

doi:10.1007/978-1-4419-5546-3_21

Cited by 21 publications

(11 citation statements)

References 60 publications

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“…We frame the issue in terms of the Knowledge-Learning-Instruction (KLI) theoretical framework (Koedinger et al 2012), a recent framework linking cognitive science and educational research to educational practice. Our discussion extends that in Aleven (2013). A fundamental hypothesis in KLI is that the complexity of the instruction needs to match the complexity of the targeted knowledge, in the sense that complex knowledge requires complex instructional processes.…”

Section: Does Help Help? Theory On Verbal Sense Making During Problemsupporting

confidence: 56%

“…It states what to do next and why that is a good thing to do, explained in terms of underlying problem-solving principles. Hints of this type are assumed to help students enhance their understanding of key concepts and principles (e.g., Aleven 2013;Anderson 1993) and reduce floundering during problem solving (Aleven and Koedinger 2000). Help seeking is important in many learning contexts other than ITSs, including social contexts, although our project focused on help use within ITSs.…”

Section: Introductionmentioning

confidence: 99%

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Help Helps, But Only So Much: Research on Help Seeking with Intelligent Tutoring Systems

Aleven

Roll

McLaren

et al. 2016

Int J Artif Intell Educ

Self Cite

143

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Help seeking is an important process in self-regulated learning (SRL). It may influence learning with intelligent tutoring systems (ITSs), because many ITSs provide help, often at the student's request. The Help Tutor was a tutor agent that gave incontext, real-time feedback on students' help-seeking behavior, as they were learning with an ITS. Key goals were to help students become better self-regulated learners and help them achieve better domain-level learning outcomes. In a classroom study, feedback on help seeking helped students to use on-demand help more deliberately, even after the feedback was no longer given, but not to achieve better learning outcomes. The work made a number of contributions, including the creation of a knowledge-engineered, rule-based, executable model of help seeking that can drive tutoring. We review these contributions from a contemporary perspective, with a theoretical analysis, a review of recent empirical literature on help seeking with ITSs, and methodological suggestions. Although we do not view on-demand, principle-based help during tutored problem solving as being as important as we once did, we still view it as helpful under certain circumstances, and recommend that it be included in ITSs. We view the goal of helping students become better self-regulated learners as one of the grand challenges in ITSs research today.

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Section: Does Help Help? Theory On Verbal Sense Making During Problemsupporting

confidence: 56%

Section: Introductionmentioning

confidence: 99%

Help Helps, But Only So Much: Research on Help Seeking with Intelligent Tutoring Systems

Aleven

Roll

McLaren

et al. 2016

Int J Artif Intell Educ

Self Cite

143

View full text Add to dashboard Cite

show abstract

“…Help seeking in online problem-solving environments (such as Khan Academy practice problems or homework sites) has different characteristics from help seeking in traditional classrooms (Aleven, 2013;Aleven et al, 2003). Mainly, when individual students work with computers, students' help requests may not carry as much of a social price tag as asking a teacher for help in front of the whole class.…”

Section: Help Seeking In Online Problem-solving Environmentsmentioning

confidence: 98%

On the Benefits of Seeking (and Avoiding) Help in Online Problem-Solving Environments

Roll

Baker

Aleven

et al. 2014

Journal of the Learning Sciences

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Seeking the right level of help at the right time can support learning. However, in the context of online problem-solving environments, it is still not entirely clear which help-seeking strategies are desired. We use fine-grained data from 38 high school students who worked with the Geometry Cognitive Tutor for 2 months to better understand the associations between specific help-seeking patterns and learning. We evaluate how students' help-seeking behaviors on each step in a tutored problem are associated with their success on subsequent steps that require the same skills. Analyzing learning at the skill level allows us to compare different help-seeking patterns within a single student, controlling for between-student variations. Overall, asking for help on challenging steps is associated with productive learning, and overusing help is associated with poorer learning. However, contrary to many help-seeking theories, avoiding help (and failing repeatedly) is associated with better

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“…Additionally, an adaptive, intelligent software system would enable both teachers and students to experience effective and efficient educational processes. It can be seen from the related literature that Artificial Intelligence is widely employed to develop such educational software tools or systems . So, the authors have focused on using Artificial Intelligence based techniques in order to develop a software system in the sense of the objectives of this study. Teaching abstract subjects may need more efforts or even more supportive materials in order to ensure necessary impact during learning processes.…”

Section: Educational Motivationsmentioning

confidence: 99%

Optimization of self‐learning in Computer Engineering courses: An intelligent software system supported by Artificial Neural Network and Vortex Optimization Algorithm

Köse

Arslan

2017

Comp Applic In Engineering

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Self-learning process is an important factor that enables learners to improve their own educational experiences when they are away of face-to-face interactions with the teacher. A well-designed selflearning activity process supports both learners and teachers to achieve educational objectives rapidly. Because of this, there has always been a remarkable trend on developing alternative self-learning approaches. In this context, this study is based on two essential objectives. Firstly, it aims to introduce an intelligent software system, which optimizes and improves computer engineering students' self-learning processes. Secondly, it aims to improve computer engineering students' self-learning during the courses. As general, the software system introduced here evaluates students' intelligence levels according to the Theory of Multiple Intelligences and supports their learning via accurately chosen materials provided over the software interface. The evaluation mechanism of the system is based on a hybrid Artificial Intelligence approach formed by an Artificial Neural Network, and an optimization algorithm called as Vortex Optimization Algorithm (VOA). The system is usable for especially technical courses taught at computer engineering departments of universities and makes it easier to teach abstract subjects. For having idea about success of the system, it has been tested with students and positive results on optimizing and improving self-learning have been obtained. Additionally, also a technical evaluation has been done previously, in order to see if the VOA is a good choice to be used in the system. It can be said that the whole obtained results encourage the authors to continue to future works. ß 2017 Wiley Periodicals, Inc. Comput Appl Eng Educ 25:142-156, 2017; View this article online at wileyonlinelibrary.com/journal/cae;

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Help Seeking and Intelligent Tutoring Systems: Theoretical Perspectives and a Step Towards Theoretical Integration

Cited by 21 publications

References 60 publications

Help Helps, But Only So Much: Research on Help Seeking with Intelligent Tutoring Systems

Help Helps, But Only So Much: Research on Help Seeking with Intelligent Tutoring Systems

On the Benefits of Seeking (and Avoiding) Help in Online Problem-Solving Environments

Optimization of self‐learning in Computer Engineering courses: An intelligent software system supported by Artificial Neural Network and Vortex Optimization Algorithm

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