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The Pépite project

The Pépite project. Evaluating the performance of a diagnostic system in School Algebra. Naima El- Kechaï , Élisabeth Delozanne , D. Prévit, B. Grugeon, F. Chenevotot. Outline. Why a cognitive diagnosis ? The Pépite project The diagnostic system A simple example PépiDiag Evaluation

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The Pépite project

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  1. The Pépite project Evaluating the performance of a diagnostic system in School Algebra Naima El-Kechaï, Élisabeth Delozanne, D. Prévit, B. Grugeon, F. Chenevotot

  2. Outline • Why a cognitive diagnosis ? • The Pépite project • The diagnostic system • A simple example • PépiDiag • Evaluation • Conclusion

  3. The Pépite project • Objective • To design and implement a web-based application • To support math teachers • To manage the students’ cognitive diversity • Domain • Algebra class • 9th-10th grade (15-16 years old)

  4. A teaching scenario • Who ? • Mary is a Math teacher in 9th grade • Context • To bring all her class to the same level • Steps • Students have the online diagnostic test • Pépite • analyzes the students’ answers • displays a cognitive profile of each student and of the whole class • suggests 6 groups of students and a session of exercises adapted to their cognitive profiles • Mary can adjust the groups and the exercises • Students log in and do the exercises in their assigned session

  5. A cognitive profile of a class • Groupe A+ • Engage in algebraic thinking • Use algebraic calculation appropriately • Groupe B+ • Begin to use algebra to solve problems • Use sometimes mal-rules • Groupe C- • Stuck in arithmetical thinking • Algebra makes no sense

  6. A B+ student’s cognitive profile More More More More More More

  7. A differentiated session

  8. Pépite : Cognitive Diagnosis Process

  9. A simple example

  10. Answer assessment (local diagnosis) Type of answers : Recognition of sub-figures but turning a product into a sum

  11. PépiDiag general architecture Solves exercises Teacher’s interface Student’s answers StudentDatabase Student’s interface Student’sanswers Answerscoding + Student’s profile PépiDiag ExerciseDatabase Exercisecoding prescription file

  12. A coding prescription file (partial)

  13. PépiDiag • Global Diagnosis • Student’s profile

  14. Outline • Why a cognitive diagnosis ? • The Pépite project • The diagnostic system • A simple example • PépiDiag • Evaluation • Conclusion

  15. Evaluation • Quality requirements • No correct answer badly coded • Better no code than a wrong one • Minimal number of answers left unanalyzed • Method • Comparison between experts & system assessment • 3 experts assessed 360 answers of each exercise (one by one) • Experts worked separately and then agreed on some corrections to minor errors

  16. Unanalyzed answers • Data collected (on the exercise used as an example) • PépiDiag analyzes • More answers than the teacher • Less than the experienced researcher

  17. Correct answers • No correct answer is analyzed incorrectly • Unanalyzed answers • 5/13 : Using X instead of * • 8/13 : Mixing algebra and natural language : • « (a+b) times (3+a) » • Solutions • To prevent students from typing letters other than those relating to the exercise statement

  18. Incorrect answers • Incorrect coding • b+ axa +3 (parenthesis errors) • b + a² + 3 (gathering the figure items) • Both are incorrect (V3) distinct for human experts and the same for the CAS • Unanalyzed : incompleteness + interface

  19. Conclusion • Our approach to asses students’ open answers • Human experts • typify anticipated patterns of correct and incorrect solutions • code each type on several dimensions • PépiDiag, using a CAS, • matches the student’s solution with a pattern • PépiDiag is reliable • 100 % : closed answers • 80 % : answers with one algebraic expression • 70 % : answers with a multi-step reasoning [ITS 2008] • PépiDiag saves teachers’ time and tedious effort

  20. Current work • http://www.sesamath.net/ • Diagnosis • To evaluate the building of the student’s profile from the answer assessment • Comparing • Heuristic method (implemented) • Statistical building (being implemented) • Neural Networks (being implemented) • A differentiation support tool for teachers • To validate with teachers the different learning paths adapted to each (group of profile) • To build a tool to automatically • retrieve indexed exercises from a data base • suggest a session adapted to a student’s profile

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