Assessment of Improvement in Vocabulary Learning with Longitudinal Big Data: Application of the Scheduling Principle Controlling Temporal Dimension Factors to Education

ID: 43131 Type: Full Paper: Conceptual & Empirical Study
  1. Takafumi Terasawa, Okayama University, Japan
  2. Yuka Kawasaki, National Institute of Technology, Kure, Japan

Tuesday, June 24 11:15-11:45 AM Location: B3117 View on map

Presider: Heli Ruokamo, Univeristy of Lapland, Finland

Abstract: In this paper, we examined a large number of data that we collected by using Microstep Assessment Technology (Microstep). Microstep is a new fundamental technology that we invented in order to collect and measure large amounts of data objectively with high accuracy. We applied this technology to language education and conducted vocabulary learning experiments. College students and adults participated in the experiment and continuously learned English vocabulary for about three months. After the learning period, the software in Microstep gathered and analyzed the data on all the events that occurred during the experiment. We found that a pattern of daily word acquisition had steadily developed over a long period while the learners were unaware of it. Moreover, we successfully depicted this pattern for each learner.


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