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Should we consider Efficiency and Constancy for Adaptation in Intelligent Tutoring Systems?

Presenter: Pedro Manuel Moreno-Marcos

Authors: Pedro Manuel Moreno-Marcos, Dánae Martínez de la Torre, Gabriel González Castro, Pedro J. Muñoz-Merino and Carlos Delgado Kloos

16th International Conference on Intelligent Tutoring Systems 10th June 2020

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INDEX

1. INTRODUCTION 2. METHODOLOGY 3. RESULTS

4. CONCLUSIONS AND FUTURE WORK

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INTRODUCTION

• Adaptation in Intelligent Tutoring Systems (ITSs) – Skill modelling

– Students’ behaviors

• Emotions

• Self-regulated learning

• Focus on two indicators:

– Efficiency – Constancy

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OBJECTIVES

• O1: Prevalence of efficiency / constancy

• O2: Evolution of efficiency / constancy over time

• O3: Relationship between efficiency / constancy and other variables (e.g., performance)

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INDEX

1. INTRODUCTION 2. METHODOLOGY 3. RESULTS

4. CONCLUSIONS AND FUTURE WORK

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EDUCATIONAL CONTEXT

• Data from K-12 students (n = 10,171)

• Interactions with exercises:

– Student identifier – Activity identifier – Timestamp

– Time spent – Grade

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EFFICIENCY

• Points achieved per unit of time

• : Best grade in exercise

• Time spent in exercise

• Number of attempted exercises

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CONSTANCY

• Inverse of the standard deviation (SD) of daily time spent doing activities

• Time spent in day

• Active days

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INDEX

1. INTRODUCTION 2. METHODOLOGY 3. RESULTS

4. CONCLUSIONS AND FUTURE WORK

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PREVALENCE

• Ranges for both variables are quite dispersed

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CLUSTER ANALYSIS: EFFICIENCY

• Six profiles are identified using hierarchical clustering

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CLUSTER ANALYSIS: CONSTANCY

• Active days and time spent are related to constancy

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• Students who spend more time → less constant

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TEMPORAL EVOLUTION

• Little variations of efficiency / constancy over time

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RELATIONSHIP WITH OTHER VARIABLES

• Analyze the relationship between efficiency / constancy and the following variables:

– active_days – avg_att

– avg_grade – avg_time_ex – naccess

– nactivities – time_spent – total_grade

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CORRELATION ANALYSIS

• Efficiency /

Constancy are related to:

– avg_time_ex – time_spent

• No relationship with grades

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RELATIONSHIP WITH TOTAL GRADES

• No strong relationship

between efficiency / constancy and grades

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REGRESSION ANALYSIS

• Regression models to predict grade:

– Efficiency and constancy: R2 = 0.11

– time_spent and avg_time_ex: R2 = 0.14 – nactivities: R2 = 0.81

• Efficiency and constancy add a different dimension from grades

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INDEX

1. INTRODUCTION 2. METHODOLOGY 3. RESULTS

4. CONCLUSIONS AND FUTURE WORK

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CONCLUSIONS

• 1) Big variation of students’ efficiency

• 2) Possibility to cluster students based on efficiency

• 3) Big variation of students’ constancy

• 4) Students who spend more time are less constant

• 5) Possibility to detect students with low engagement and constancy

• 6) More efficient students tend to be more constant

• 7) Efficiency and constancy are not related to grades

• 8) Adaptation based on efficiency and constancy can make sense

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LIMITATIONS AND FUTURE WORK

• LIMITATIONS

– Different course contexts – Definition of the variables

• FUTURE WORK

– Analysis in courses with known methodology

– Carry out adaptation and interventions based on efficiency/constancy

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Thank you for

your attention!

Referencias

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