# Triangulating multimodal data: Data of interaction logs, learning achievement, and motivation of L2 learners in a computer-assisted language learning environment

## TL;DR

This paper presents a dataset combining interaction logs, learning outcomes, and motivation data from L2 learners using an ICALL system for English reading.

## Contribution

The novelty lies in triangulating multimodal data to explore the relationship between digital learning behaviors, motivation, and achievement in L2 learners.

## Key findings

- Digital traces can predict learning achievement in L2 learners.
- Motivation interacts with digital reading behaviors in online learning.
- Clickstream data captures detailed learner engagement patterns.

## Abstract

This dataset comprises detailed interaction log data from 201 learners engaged in second language (L2) English reading assignments with an intelligent computer-assisted language learning (ICALL) system over a six-week period. After preprocessing, a total of 116,168 clickstream data points were generated, capturing learners’ behaviours, such as navigation patterns and task engagement within the system. In addition to the interaction logs, the dataset includes learners’ L2 reading proficiency test scores collected before and after the learning period and self-reported measures of motivation toward the subject. These multimodal data provide valuable insights into how learners engage with online learning materials, how person-level factors such as motivation interact with digital reading behaviours, and how digital traces can be used to predict learning achievement.