Locus of feedback control in computer-based tutoring: impact on learning rate, achievement and attitudes

By Albert T. Corbett, John R. Anderson. International Conference on Human Factors in Computing Systems, 2001

347 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Science Applications

View Paper: https://www.semanticscholar.org/paper/2a5d1b64a18f78c20570efccc4be78c3401fdc5c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computer Science Applications

Citations in the corpus

Step Tutor: Supporting Students through Step-by-Step Example-Based Feedback (2020, background): “These hints can help students progress when stuck [37, 38], and sometimes achieve better learning [14], especially when students engage in self-explanation of the hint [33].”
Fostering the Intelligent Novice: Learning From Errors With Metacognitive Tutoring (2005, background): “Other researchers have observed that human tutors let learners engage in activities associated with error detection and correction (Fox 1993). Furthermore, research on human tutoring strategies shows that tutors try to instill a sense of control in learners (Lepper &Chabay, 1988). Immediate feedback”
Focus on Formative Feedback (2007, methodology): “…of elaboration, responsespecific feedback, appears to enhance student achievement, particularly learning efficiency, more than other types of feedback, such as simple verification or “answer until correct” (e.g., Corbett & Anderson, 2001; Gilman, 1969; Mory, 2004; Shute, Hansen, & Almond, 2007).”
The Knowledge-Learning-Instruction Framework: Bridging the Science-Practice Chasm to Enhance Robust Student Learning (2012, background): “…KCs (e.g., perceptual categories, simple concepts and skills) may imply instructional approaches that emphasize recall (Roediger & Karpicke, 2006), spacing of practice (Cepeda et al., 2006), tutored practice (e.g., Corbett & Anderson, 2001), and optimized scheduling of practice (Pavlik, 2007).”
Teaching Johnny not to fall for phish (2010, background): “showed that students who got immediate feedback performed significantly better than students who got delayed feedback [Corbett and Anderson 2001].”
Inventing to Prepare for Future Learning: The Hidden Efficiency of Encouraging Original Student Production in Statistics Instruction (2004, background): “This has led many to question their value and some to advocate correcting student errors as quickly as possible (e.g., Corbett & Anderson, 2001; for discussion see Barron et al., 1998; National Research Council [NRC], 2001; Vollmeyer, Burns, & Holyoak, 1996).”
Anti-Phishing Landing Page : Turning a 404 into a Teachable Moment for End Users (2009, positive): “showed that students who received immediate feedback performed significantly better than students who received delayed feedback [6].”
When immediate interactive feedback boosts optimization problem solving: A 'human-in-the-loop' approach for solving Capacitated Vehicle Routing Problems (2017, methodology): “Corbett and Anderson (2001) posited that immediate corrective feedback provided by computer systems (e.g. intelligent computer tutors) during problem solving resulted in faster time completion.”
The Impact of Adding Textual Explanations to Next-step Hints in a Novice Programming Environment (2019, background): “[9] showed that students who received various forms of feedback while using the system completed subsequent programming”
Current practice in measuring usability: Challenges to usability studies and research (2006, methodology): “In this study, users of a tutoring system aimed at supporting programming answered on a seven-point scale two questions: ‘‘How difﬁcult were the exercises’’ and ‘‘Did the tutor help you ﬁnish more quickly’’ (Corbett and Anderson, 2001, p. 251).”