{"title":"Associations of Research Questions, Analytical Techniques, and Learning Insight in Temporal Educational Research","authors":"Sina Nazeri, M. Hatala, Carman Neustaedter","doi":"10.18608/jla.2023.7745","DOIUrl":null,"url":null,"abstract":"Learning has a temporal characteristic in nature, which means that it occurs over the passage of time. The research on the temporal aspects of learning faces several challenges, one of which is utilizing appropriate analytical techniques to exploit the temporal data. There is no coherent guide to selecting certain temporal techniques to lead to results that truthfully uncover underlying phenomena. To fill this gap, this systematic mapping study contributes to understanding the type of questions and approaches in works in the area of temporal educational research. This study aims to analyze different components of published research and explores the current trends in educational studies that explicitly consider the temporal aspect. Using the thematic coding method, we identified trends in three components, including asked research questions, utilized methodological techniques, and inferred insight about learning. The distribution of codes regarding asked research questions showed that the highest number of studies focused on method development or proposing a methodological framework. We discussed that methodological development, with the underlying theory, led to identifying learning indicators that can provide the ability to identify individual students with respect to the learning concepts of interest. In terms of utilized techniques, there was a strong trend in visualization analysis and process mining. This study found that to discover insight into learning, it is important to utilize techniques that are interpretable to characterize temporal patterns.","PeriodicalId":36754,"journal":{"name":"Journal of Learning Analytics","volume":" ","pages":""},"PeriodicalIF":2.9000,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Learning Analytics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.18608/jla.2023.7745","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"EDUCATION & EDUCATIONAL RESEARCH","Score":null,"Total":0}
引用次数: 0
Abstract
Learning has a temporal characteristic in nature, which means that it occurs over the passage of time. The research on the temporal aspects of learning faces several challenges, one of which is utilizing appropriate analytical techniques to exploit the temporal data. There is no coherent guide to selecting certain temporal techniques to lead to results that truthfully uncover underlying phenomena. To fill this gap, this systematic mapping study contributes to understanding the type of questions and approaches in works in the area of temporal educational research. This study aims to analyze different components of published research and explores the current trends in educational studies that explicitly consider the temporal aspect. Using the thematic coding method, we identified trends in three components, including asked research questions, utilized methodological techniques, and inferred insight about learning. The distribution of codes regarding asked research questions showed that the highest number of studies focused on method development or proposing a methodological framework. We discussed that methodological development, with the underlying theory, led to identifying learning indicators that can provide the ability to identify individual students with respect to the learning concepts of interest. In terms of utilized techniques, there was a strong trend in visualization analysis and process mining. This study found that to discover insight into learning, it is important to utilize techniques that are interpretable to characterize temporal patterns.