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Šaric-Grgic, Ines; Grubišic, Ani; Šeric, Ljiljana; Robinson, Timothy J. – International Journal of Distance Education Technologies, 2020
The idea of clustering students according to their online learning behavior has the potential of providing more adaptive scaffolding by the intelligent tutoring system itself or by a human teacher. With the aim of identifying student groups who would benefit from the same intervention in AC-ware Tutor, this research examined online learning…
Descriptors: Learning Analytics, Intelligent Tutoring Systems, Grouping (Instructional Purposes), Undergraduate Students
Shimada, Atsushi; Mouri, Kousuke; Taniguchi, Yuta; Ogata, Hiroaki; Taniguchi, Rin-ichiro; Konomi, Shin'ichi – International Educational Data Mining Society, 2019
In this paper, we focus on optimizing the assignment of students to courses. The target courses are conducted by different teachers using the same syllabus, course design, and lecture materials. More than 1,300 students are mechanically assigned to one of ten courses taught by different teachers. Therefore, mismatches often occur between students'…
Descriptors: Student Placement, Learning Activities, Learning Analytics, Cognitive Style
Krassadaki, Evangelia; Lakiotaki, Kleanthi; Matsatsinis, Nikolaos F. – European Journal of Engineering Education, 2014
Peer assessment (PA), as formative procedure, enhances learning by providing students with the opportunity to peer assess each other's work. However, since students exhibit different value systems (abilities, experiences, attitudes, cognitive styles, etc.) we propose a diagnostic procedure, which can be applied at the beginning of a course, in…
Descriptors: Foreign Countries, Peer Evaluation, Student Behavior, Student Attitudes
Elphinstone, Brad; Tinker, Sean – Journal of College Student Development, 2017
The Motivation and Engagement Scale-University/College (MES-UC) was used to identify student typologies on the basis of adaptive and maladaptive academic cognitions and behaviours. The sample comprised first-year (n = 390), second-year (n = 300), and third-year (n = 251) undergraduate students with 4 student typologies identified: high…
Descriptors: Student Motivation, Undergraduate Students, Likert Scales, Cohort Analysis
Riofrio-Luzcando, Diego; Ramirez, Jaime; Berrocal-Lobo, Marta – IEEE Transactions on Learning Technologies, 2017
Data mining is known to have a potential for predicting user performance. However, there are few studies that explore its potential for predicting student behavior in a procedural training environment. This paper presents a collective student model, which is built from past student logs. These logs are first grouped into clusters. Then, an…
Descriptors: Student Behavior, Predictive Validity, Predictor Variables, Predictive Measurement
Acquah, Emmanuel O.; Palonen, Tuire; Lehtinen, Erno; Laine, Kaarina – Scandinavian Journal of Educational Research, 2014
The focus of our study is social status among first graders. In particular, we will consider the relationship between acceptance and rejection, and how these are connected to three social behavioral traits: bullying, victimization, and social withdrawal. The data set is from peer nominations of 748 children from 49 classrooms in the southwest of…
Descriptors: Social Status, Profiles, Grade 1, Social Behavior