In the context of multiple intelligences theory, intelligent data analysis of learning styles was based on rough set theory

The present study aims to identify the relationship between individuals' multiple intelligence areas and their learning styles with mathematical clarity using the concept of rough sets which is used in areas such as artificial intelligence, data reduction, discovery of dependencies, prediction of data significance, and generating decision (control) algorithms based on data sets. Therefore, first multiple intelligence areas and learning styles of 243 mathematics prospective teachers studying at a state university were identified using the “Multiple Intelligence Inventory for Educators” developed by Armstrong and the “Learning Styles Scale” developed by Kolb. Second, the data was appropriated for rough set analysis and we identified potential learning styles that a student can have based on the learning style s/he already has. Certainty degrees of the learning style sets were α_R(D) ≅ 0.717, α_R(C) ≅ 0.618, α_R(AS) ≅ 0.699, α_R(AC) ≅ 0.461, and these sets were found to be rough sets. Finally, decision rules were identified for multiple intelligences and learning styles.     

Mainstream and immigrant students’ primary school mathematics achievement differences in European countries

European Journal of Psychology of Education - The few studies on the achievement differences of mainstream and immigrant primary school students in large-scale assessments point to an achievement...     

On constituent chunking for Turkish

Chunking is a task which divides a sentence into non-recursive structures. The primary aim is to specify chunk boundaries and classes. Although chunking generally refers to simple chunks, it is possible to customize the concept. A simple chunk is a small structure, such as a noun phrase, while constituent chunk is a structure that functions as a single unit in a sentence, such as a subject. For an agglutinative language with a rich morphology, constituent chunking is a significant problem in comparison to simple chunking. Most of Turkish studies on this issue use the IOB tagging schema to mark the boundaries.In this study, we proposed a new simpler tagging schema, namely OE, in constituent chunking for Turkish. “E” represents the rightmost token of a chunk, while “O” stands for all other items. In reference to OE, we also used a schema called OB, where “B” represents the leftmost token of a chunk. We aimed to identify both chunk boundaries and chunk classes using the conditional random fields (CRF) method. The initial motivation was to employ the fact that Turkish phrases are head-final for chunking. In this context, we assumed that marking the end of a chunk (OE) would be more advantageous than marking the beginning of a chunk (OB). In support of the assumption, the test results reveal that OB has the worst performance and OE is significantly a more successful schema in many cases. Especially in long sentences, this contrast is more obvious. Indeed, using OE means simply marking the head of the phrase (chunk). Since the head and the distinctive label “E” are aligned, CRF finds the chunk class more easily by using the information contained in the head. OE also produced more successful results than the schemas available in the literature.In addition to comparing tagging schemas, we performed four analyses. Along with the examination of window size, which is a parameter of CRF, it is adequate to select and accept this value as 3. A comparison of the evaluation measures for chunking revealed that F-score was a more balanced measure in contrast to token accuracy and sentence accuracy. As a result of the feature analysis, syntactic features improves chunking performance significantly under all conditions. Yet when withdrawing these features, a pronounced difference between OB and OE is forthcoming. In addition, flexibility analysis shows that OE is more successful in different data.     

Social cognitive predictors of pre-service teachers’ technology integration performance

The main objective of the study was to examine interrelationships among social cognitive variables (self-efficacy, outcome expectations, and performance goals) and their role in predicting pre-service teachers’ technology integration performance. Although researchers have examined the role of these variables in the teacher-education context, the present study was an examination of the manner in which variables may jointly function to predict technology integration performance. The Social Cognitive Career Theory (SCCT) served as the theoretic framework. Participants were 111 pre-service teachers enrolled in an introductory instructional technology course. Findings revealed that SCCT predictions were largely supported when the freshman students were excluded from the analyses. Self-efficacy and outcome expectations were related to each other and both contributed to the prediction of performance.     

Predictors of e-democracy applicability in Turkish K-12 schools

Education and Information Technologies - Today, the COVID-19 pandemic has paved the way for a more democratic climate in K-12 schools. Administrators and teachers have had to seek out new ways...     

Using cluster analysis for data mining in educational technology research

Cluster analysis is a group of statistical methods that has great potential for analyzing the vast amounts of web server-log data to understand student learning from hyperlinked information resources. In this methodological paper we provide an introduction to cluster analysis for educational technology researchers and illustrate its use through two examples of mining click-stream server-log data that reflects student use of online learning environments. Cluster analysis can be used to help researchers develop profiles that are grounded in learner activity??like sequence for accessing tasks and information, or time spent engaged in a given activity or examining resources??during a learning session. The examples in this paper illustrate the use of a hierarchical clustering method (Ward??s clustering) and a non-hierarchical clustering method (k-Means clustering) to analyze characteristics of learning behavior while learners engage in a problem-solving activity in an online learning environment. A discussion of advantages and limitations of using cluster analysis as a data mining technique in educational technology research concludes the article.