Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Modeling Grammatical Hypothesis Testing in Young Learners: A Sequence-Based Learning Analytics Study of Morphosyntactic Reasoning in an Interactive Game

Thierry Geoffre, Trystan Geoffre

TL;DR

This study investigates grammatical reasoning in primary school learners through a sequence-based learning analytics approach, leveraging fine-grained action sequences from an interactive game targeting morphosyntactic agreement in French, revealing that determiners and verbs are key sites of difficulty.

Abstract

This study investigates grammatical reasoning in primary school learners through a sequence-based learning analytics approach, leveraging fine-grained action sequences from an interactive game targeting morphosyntactic agreement in French. Unlike traditional assessments that rely on final answers, we treat each slider movement as a hypothesis-testing action, capturing real-time cognitive strategies during sentence construction. Analyzing 597 gameplay sessions (9,783 actions) from 100 students aged 8-11 in authentic classroom settings, we introduce Hamming distance to quantify proximity to valid grammatical solutions and examine convergence patterns across exercises with varying levels of difficulty. Results reveal that determiners and verbs are key sites of difficulty, with action sequences deviating from left-to-right usual treatment. This suggests learners often fix the verb first and adjust preceding elements. Exercises with fewer solutions exhibit slower and more erratic convergence, while changes in the closest valid solution indicate dynamic hypothesis revision. Our findings demonstrate how sequence-based analytics can uncover hidden dimensions of linguistic reasoning, offering a foundation for real-time scaffolding and teacher-facing tools in linguistically diverse classrooms.

Modeling Grammatical Hypothesis Testing in Young Learners: A Sequence-Based Learning Analytics Study of Morphosyntactic Reasoning in an Interactive Game

TL;DR

This study investigates grammatical reasoning in primary school learners through a sequence-based learning analytics approach, leveraging fine-grained action sequences from an interactive game targeting morphosyntactic agreement in French, revealing that determiners and verbs are key sites of difficulty.

Abstract

This study investigates grammatical reasoning in primary school learners through a sequence-based learning analytics approach, leveraging fine-grained action sequences from an interactive game targeting morphosyntactic agreement in French. Unlike traditional assessments that rely on final answers, we treat each slider movement as a hypothesis-testing action, capturing real-time cognitive strategies during sentence construction. Analyzing 597 gameplay sessions (9,783 actions) from 100 students aged 8-11 in authentic classroom settings, we introduce Hamming distance to quantify proximity to valid grammatical solutions and examine convergence patterns across exercises with varying levels of difficulty. Results reveal that determiners and verbs are key sites of difficulty, with action sequences deviating from left-to-right usual treatment. This suggests learners often fix the verb first and adjust preceding elements. Exercises with fewer solutions exhibit slower and more erratic convergence, while changes in the closest valid solution indicate dynamic hypothesis revision. Our findings demonstrate how sequence-based analytics can uncover hidden dimensions of linguistic reasoning, offering a foundation for real-time scaffolding and teacher-facing tools in linguistically diverse classrooms.
Paper Structure (18 sections, 19 figures, 10 tables)

This paper contains 18 sections, 19 figures, 10 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Development of the Morphographic Competency in French
  4. Game-Based Assessment and the Visibility of Cognitive Strategies
  5. From Final Answers to Action Sequences in Learning Analytics
  6. Synthesis and Gap
  7. Methodology
  8. Platform and Game: Tirettes
  9. Data
  10. Results
  11. General Results
  12. Tendencies: Overview
  13. Tendencies: The Hamming Distance
  14. Tendencies: The Convergence
  15. Overall Convergence
  16. ...and 3 more sections

Figures (19)

  • Figure 1: Different positions of sliders creating a non-grammatical sentence (vector [2,2,1,3,3,3,1,1], see 4.3)
  • Figure 2: Different positions of sliders creating a grammatical sentence (vector [2,2,1,1,2,2,2,2], see 4.3)
  • Figure 3: What the player sees with positive (green) feedback
  • Figure 4: What the player sees with negative (red) feedback
  • Figure 5: General overview of results showing errors by grammatical category
  • ...and 14 more figures