Assessing the Impact of Positive Feedback in Constraint-Based Tutors

Abstract

Across many domains, Intelligent Tutoring Systems (ITSs) are used to facilitate practice, providing a customized learning environment and personal tutoring experience for students to learn at their own pace through effective student modeling and feedback. queries. An evaluation study was conducted at the University of Canterbury involving a control group of students who used the original version of SQL-Tutor giving only negative feedback and an experimental group using the modified version of SQL-Tutor where both negative and positive feedback were given. Results of the study show that students learn quite similarly from one system to another, however those in the experimental group take significantly less time to solve the same number of problems, in fewer attempts compared to those in the control group. Students in the experimental group also learn approximately the same number of concepts as students in the control but in much less time. This indicates that positive feedback results in increased amount of learning over a shorter period of time and improves the effectiveness of learning in ITSs.

Evaluation study of eLGuide: A framework for adaptive e‐learning

“…The most important of these are the course completion time, the overall number of navigation steps, and concept repetitions during the course. In accordance with the results of similar experiments, like Barrow and Mitrovic , we expected that the completion time, number of steps, and concept repetition should decrease for the experimental group (using the adaptive version of eLGuide). A summary of the results obtained is given in Table .…”

“…We used two statistical techniques for this analysis: T‐test : used to compare performance parameters such as pre/post‐test scores, completion time, number of steps and concept repetitions, of experimental and control groups in similar projects; see, for example, Norusis , Wolf , and Barrow . Effect size : used by many researchers in the field of computer‐based educational systems to compare learning gains; see, for example, Mayer and Wolf . In order to reflect the sense of magnitude of an effect and to facilitate interpretation, Cohen proposed a rule‐of‐thumb categorization in “small” (0.2), “medium” (0.5), and “large” (0.8) effects.…”

“…T‐test : used to compare performance parameters such as pre/post‐test scores, completion time, number of steps and concept repetitions, of experimental and control groups in similar projects; see, for example, Norusis , Wolf , and Barrow .…”

“…T-test [16]: used to compare performance parameters such as pre/post-test scores, completion time, number of steps and concept repetitions, of experimental and control groups in similar projects; see, for example, Norusis [58], Wolf [35], and Barrow [55]. Effect size [35]: used by many researchers in the field of computer-based educational systems to compare learning gains; see, for example, Mayer [59] and Wolf [35].…”

How feedback boosts motivation and play in a brain-training game

“…This indicates that positive (vs. negative) feedback could also satisfy a feeling of autonomy in recipients. Furthermore, participants who receive positive feedback during a learning task also complete that task faster than (and at the same level of accuracy as) participants who received negative feedback (Barrow, Mitrovic, Ohlsson, & Grimley, 2008).…”