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Question Training

Question Training. Jeremiah Sullins The University of Memphis, USA July 6, 2009. Outline. Question training (Experiment 1) Question training module (Experiment 2). Student Questions.

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Question Training

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  1. Question Training Jeremiah Sullins The University of Memphis, USA July 6, 2009

  2. Outline • Question training (Experiment 1) • Question training module (Experiment 2)

  3. Student Questions • Question generation is infrequent and unsophisticated in traditional classroom-like settings (e.g., Baker, 1979; Dillon, 1988; Graesser & Person, 1994; Van der Meij, 1988). • Graesser and Person (1994) pointed that an individual student would ask 1 question in a little less than 10 hours of class time (that’s less than a question every school day!).

  4. Question Training • However…There is reason to be optimistic! • Training a student to ask questions as they read can lead to comprehension gains (e.g. King 1996). • Craig, Gholson, Ventura, Graesser, & TRG (2000) used vicarious learning procedures to efficiently invoke question asking in learners in a relatively brief period of time (about 30 minutes)

  5. Question Training (Experiment 1) • Briner & McNamara (2007) • Explored the effects of question training on comprehension for high and low domain knowledge learners

  6. Question Training (Experiment 1) • Interest Questionnaire • Gates-MacGinitie Reading Test • Prior Science Knowledge Questionnaire • Pretest • Question Training or Control • Posttest • Debriefing

  7. Question Training (Experiment 1) • Questions based on PREG model (Otero & Graesser, 2001) • Deep: Misunderstood causal relations, misunderstood links, clashes between the text and the readers’ knowledge (e.g., “When it rains, light impinges on water drops and a rainbow is formed. Why is it that clouds are white and a rainbow is colored?”) • Shallow: Unknown words and incomprehensible statements (e.g., “What does asphyxia mean?”)

  8. Question Training (Experiment 1) • Results suggested that training high knowledge students to ask deep reasoning questions resulted in higher comprehension on posttest • Will the same results hold when using animated agents in a computer based learning environment?

  9. Question Training Module (Experiment 2) • Currently developing a interface designed to teach students to ask deep-reasoning questions • Based on iSTART (McNamara, Levinstein, & Boonthum, 2004)

  10. Question Training Module (Experiment 2) • 3 different phases: Introduction, Demonstration, & Practice • Classifies student questions on: • Deep/Shallow • Good/Medium/Bad (relevance to text) • Too similar questions • Metacognitive expressions (e.g., “I don’t get it”, “I’m lost”)

  11. Algorithm Reliability • Question classification accuracy: • Birds text 82.34% • Bees text 88.48% • 98% accuracy with “too similar” questions

  12. Procedure • Interest Questionnaire • Gates-MacGinitie Reading Test • Prior Science Knowledge Questionnaire • Pretest • Question Module or Control • Posttest • Debriefing

  13. Future Directions • Continue collecting data • In Experiment 1 low domain knowledge students learned how to ask “better” questions but did not show significant improvements in comprehension • By combining the iSTART module with the question training module, we could scaffold the low knowledge people to identify their gaps in knowledge and ask questions about those gaps, and then the iSTART module will help them fill those gaps by way of self-explanation.

  14. Any questions???

  15. Cognitive Disequilibrium & Question Asking • Cognitive disequilibrium literature suggests student questioning can significantly increase (Otero & Graesser, 2001; Graesser & McMahen, 1993; Wisher & Graesser, 2005).

  16. What is Cognitive Disequilibrium? • Cognitive disequilibrium is a situation in which learners are presented with obstacles to goals, anomalous events, contradictions, discrepancies, and obvious gaps in knowledge with the learner’s zone of proximal development (Rogoff, 1990; Vygotsky, 1978).

  17. AutoTutor • Intelligent tutoring system developed at U of M that interacts with learners’ in natural language • Tutors students in computer literacy, critical thinking, physics, research methods

  18. AutoTutor

  19. Cognitive Disequilibrium Autotutor • 3 different version of AutoTutor • False Information • False Feedback • Regular (control)

  20. Peer versus Expert • Addressee of question could be another factor influencing student questions (Lee, 1997; Van der Meij, 2005) • A second factor we will be exploring with this study is the “role” of a confederate (peer versus expert)

  21. Cognitive Disequilibrium AutoTutor • Questions based on a question taxonomy by Graesser and Person (1994) • 18 different question categories

  22. Procedure • Big five personality test • Motivated strategies learning questionnaire • Pretest • Randomly assigned to AutoTutor • Agent persona questionnaire • Posttest • Debriefing

  23. Any questions???

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