A Teaching Model Exploiting Cognitive Conflict Driven by a Bayesian Network

Stacey, Kaye; Sonenberg, Ea; Nicholson, Ann E.; Boneh, Tal; Steinle, Vicki

doi:10.1007/3-540-44963-9_48

Cited by 13 publications

(13 citation statements)

References 7 publications

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“…The details of the Bayes Net are discussed in Goguadze et al, 2011. Our approach was inspired by the similar implementation of Stacey, Sonenberg, Nicholson, Boneh, and Steinle (2003).…”

Section: Do Students Differ In Misconceptions They Have?mentioning

confidence: 99%

Using erroneous examples to improve mathematics learning with a web-based tutoring system

Adams

McLaren

Durkin

et al. 2014

Computers in Human Behavior

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“…The details of the Bayes Net are discussed in Goguadze et al, 2011. Our approach was inspired by the similar implementation of Stacey, Sonenberg, Nicholson, Boneh, and Steinle (2003).…”

Section: Do Students Differ In Misconceptions They Have?mentioning

confidence: 99%

Using erroneous examples to improve mathematics learning with a web-based tutoring system

Adams

McLaren

Durkin

et al. 2014

Computers in Human Behavior

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“…The details of the Bayes Net are discussed in [23]. Our approach was inspired by the similar implementation of Stacey et al [24].…”

Section: Future Workmentioning

confidence: 99%

To Err Is Human, to Explain and Correct Is Divine: A Study of Interactive Erroneous Examples with Middle School Math Students

McLaren

Adams

Durkin

et al. 2012

Lecture Notes in Computer Science

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Abstract.Erroneous examples are an instructional technique that hold promise to help children learn. In the study reported in this paper, sixth and seventh grade math students were presented with erroneous examples of decimal problems and were asked to explain and correct those examples. The problems were presented as interactive exercises on the Internet, with feedback provided on correctness of the student explanations and corrections. A second (control) group of students were given problems to solve, also with feedback on correctness. With over 100 students per condition, an erroneous example effect was found: students who worked with the interactive erroneous examples did significantly better than the problem solving students on a delayed posttest. While this finding is highly encouraging, our ultimate research question is this: how can erroneous examples be adaptively presented to students, targeted at their most deeply held misconceptions, to best leverage their effectiveness? This paper discusses how the results of the present study will lead us to an adaptive version of the erroneous examples material.

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“…If there is not a good balance, feelings of anxiety (with high challenge and low ability) and boredom or uninvolvement (with low challenge and high proficiency) will be the result [22]. Moderately challenging tasks, i.e., tasks that are somewhat beyond the learner's current proficiency [20,21,23], make learners, on the one hand, aware that they lack some proficiency but on the other hand keep them involved [24,25]. Hence, those intermediate difficult problems engage learners and lead to a greater enjoyment of the task.…”

Section: Adaptive Item Sequencing In Item-based Learning Environmentsmentioning

confidence: 99%

When Easy Becomes Boring and Difficult Becomes Frustrating: Disentangling the Effects of Item Difficulty Level and Person Proficiency on Learning and Motivation

Moeyaert

Wauters²,

Desmet

et al. 2016

Systems

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Abstract:The research on electronic learning environments has evolved towards creating adaptive learning environments. In this study, the focus is on adaptive curriculum sequencing, in particular, the efficacy of an adaptive curriculum sequencing algorithm based on matching the item difficulty level to the learner's proficiency level. We therefore explored the effect of the relative difficulty level on learning outcome and motivation. Results indicate that, for learning environments consisting of questions focusing on just one dimension and with knowledge of correct response, it does not matter whether we present easy, moderate or difficult items or whether we present the items with a random mix of difficulty levels, regarding both learning and motivation.

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A Teaching Model Exploiting Cognitive Conflict Driven by a Bayesian Network

Cited by 13 publications

References 7 publications

Using erroneous examples to improve mathematics learning with a web-based tutoring system

Using erroneous examples to improve mathematics learning with a web-based tutoring system

To Err Is Human, to Explain and Correct Is Divine: A Study of Interactive Erroneous Examples with Middle School Math Students

When Easy Becomes Boring and Difficult Becomes Frustrating: Disentangling the Effects of Item Difficulty Level and Person Proficiency on Learning and Motivation

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