Off-task behavior in the cognitive tutor classroom

Baker, Ryan S.; Corbett, Albert T.; Koedinger, Kenneth R.; Wagner, Angela Z.

doi:10.1145/985692.985741

Cited by 320 publications

(229 citation statements)

References 12 publications

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“…It may be interesting and productive to investigate changes in motivational factors associated with adding worked examples to tutors, for instance, does doing so reduce "gaming the system" behaviors (Baker et al 2004)?…”

Section: Discussionmentioning

confidence: 99%

Accounting for Beneficial Effects of Worked Examples in Tutored Problem Solving

et al. 2010

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Recent studies have tested the addition of worked examples to tutored problem solving, a more effective instructional approach than the untutored problem solving used in prior worked example research. These studies involved Cognitive Tutors, software designed to support problem solving while minimizing extraneous cognitive load by providing prompts for problem sub-goals, step-based immediate feedback, and context-sensitive hints. Results across eight studies in three different domains indicate that adding examples to Cognitive Tutors is beneficial, particularly for decreasing the instructional time needed and perhaps also for achieving more robust learning outcomes. These studies bolster the practical importance of examples in learning, but are also of theoretical interest. By using a stronger control condition than previous studies, these studies provide a basis for refining Cognitive Load Theory explanations of the benefits of examples. Perhaps, in addition to other reasons, examples may help simply because they more quickly provide novices with information needed to induce generalized knowledge. Keywords Worked examples . Cognitive tutors . Cognitive load theoryIn recent years, a considerable number of studies have explored the conditions under which examples aid in acquiring cognitive skills (for a review, see Atkinson et al. 2000;Renkl 2005Renkl , 2010. Among other things, researchers have discovered (1) that examples are more effective than problems in the early stages of skill acquisition, (2) that problems are more effective in later stages (e.g., Kalyuga et al. 2003), (3) that interleaving examples and problems (i.e., example-problem pairs) is more effective than presenting examples and problems in blocks (Trafton and Reiser 1993), (4) that a gradual step-by-step fading of Educ Psychol Rev

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Section: Discussionmentioning

confidence: 99%

Accounting for Beneficial Effects of Worked Examples in Tutored Problem Solving

et al. 2010

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“…We collected data regarding student affective states using the quantitative field observation methods discussed in Baker, et al [1]. The observations were carried out by a team of six observers, working in pairs.…”

Section: Quantitative Field Observationsmentioning

confidence: 99%

The Affective and Learning Profiles of Students Using an Intelligent Tutoring System for Algebra

Lagud

Rodrigo

2010

Intelligent Tutoring Systems

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Abstract. We investigate whether high-performing students' experience of affect (boredom, confusion, delight, flow, frustration, neutrality and surprise) is different from that of low-performing students while using Aplusix, an ITS for Algebra. We found that students with the highest number of correct answers experienced flow the most while students with the lowest number of correct answers experienced confusion and boredom the most. Students who attempted the most difficult problems experienced flow the most while students who tried the lowest levels experienced more boredom and confusion. Students who took the longest time in solving the algebra problems experienced confusion the most while students who took the shortest time experienced confusion the least. Students who used the most number of steps to solve a problem experienced confusion and boredom the most. Students who used the least number of steps experienced more flow.

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“…Some types of poor metacognitive decisions include avoiding or misusing help [1,16] and attempting to obtain correct answers without thinking through the material (termed "gaming the system" [4,5]). There is evidence that these types of unproductive metacognitive decisions negatively affect learning.…”

Section: Metacognition In Intelligent Tutoring Systemsmentioning

confidence: 99%

“…Specifically, this detector identifies whether a student is attempting to game the system -attempting to succeed on the assigned problems by systematically exploiting properties and regularities in the system, rather than by thinking about the material. Gaming the system has been found to have a greater negative impact on learning than several types of off task behaviors [5]. Once the system detects that a student is gaming, the system can then adapt to encourage the student to use the software more appropriately.…”

Section: The Gaming Detectormentioning

confidence: 99%

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Modeling Students’ Metacognitive Errors in Two Intelligent Tutoring Systems

Roll

Baker

Aleven

et al. 2005

Lecture Notes in Computer Science

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Abstract. Intelligent tutoring systems help students acquire cognitive skills by tracing students' knowledge and providing relevant feedback. However, feedback that focuses only on the cognitive level might not be optimal -errors are often the result of inappropriate metacognitive decisions. We have developed two models which detect aspects of student faulty metacognitive behavior: A prescriptive rational model aimed at improving help-seeking behavior, and a descriptive machine-learned model aimed at eliminating attempts to "game" the tutor. In a comparison between the two models we found that while both successfully identify gaming behavior, one is better at characterizing the types of problems students game in, and the other captures a larger variety of faulty behaviors. An analysis of students' actions in two different tutors suggests that the help-seeking model is domain independent, and that students' behavior is fairly consistent across classrooms, age groups, domains, and task elements. Metacognition in Intelligent Tutoring SystemsIntelligent tutoring systems offer support and guidance to learners attempting to master a cognitive skill [7,11]. When students ask for help or make a mistake in such a tutor, they receive feedback on their problem-solving actions, that is, they receive feedback at the cognitive level. However, mistakes can also be made at the higher metacognitive level, which coordinates the learning process. Such metacognitive skills include self-assessment and help-seeking strategies, among many others. When cognitive errors originate from an incorrect metacognitive decision, feedback on student metacognition would be more appropriate. A tutoring system should try to improve students' metacognitive skills, by, for example, guiding a student who avoids using help to seek help at the right moment.Several studies have shown that students often make unproductive metacognitive decisions, which affect their learning process [9,13]. Some types of poor metacognitive decisions include avoiding or misusing help [1,16] and attempting to obtain correct answers without thinking through the material (termed "gaming the system" [4,5]). There is evidence that these types of unproductive metacognitive decisions

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Off-task behavior in the cognitive tutor classroom

Cited by 320 publications

References 12 publications

Accounting for Beneficial Effects of Worked Examples in Tutored Problem Solving

Accounting for Beneficial Effects of Worked Examples in Tutored Problem Solving

The Affective and Learning Profiles of Students Using an Intelligent Tutoring System for Algebra

Modeling Students’ Metacognitive Errors in Two Intelligent Tutoring Systems

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