Blended learning, e-learning and mobile learning in mathematics education

In this literature survey we focus on identifying recent advances in research on digital technology in the field of mathematics education. To conduct the survey we have used internet search engines with keywords related to mathematics education and digital technology and have reviewed some of the main international journals, including the ones in Portuguese and Spanish. We identify five sub-areas of research, important trends of development, and illustrate them using case studies: mobile technologies, massive open online courses (MOOCs), digital libraries and designing learning objects, collaborative learning using digital technology, and teacher training using blended learning. These examples of case studies may help the reader to understand how recent developments in this area of research have evolved in the last few years. We conclude the report discussing some of the implications that these digital technologies may have for mathematics education research and practice as well as making some recommendations for future research in this area

Cogskillnet: An Ontology-Based Representation Of Cognitive Skills

A number of studies emphasized the need to capture learners' interaction patterns in order to personalize their learning process as they study through learning objects. In education context, learning materials are designed based on pre-determined expectations and learners are evaluated to what extent they master these expectations. Representation of these expectations in learning and assessment objects, on the other hand, is a new challenge for e-learner providers. In order to address this challenge, POLEonto (Personlized Ontological Learning Environment) proposes a new method to separate these expectations by determining domain concepts (ConceptNet) and cognitive skills (CogSkillNet) for expectations via creating cognitive skill and concept ontology for K-12 education. In this paper, we report only the development and design processes of CogSkillNet within POLEonto environment; and, we further discuss how CogSkillNet can be modeled in the e-learning domain. We also describe how ontological representations play a role in creating personalized navigational guidance for allowing visualization of cognitive skills and providing useful navigational feedback to learners.Wo

Learning Programming, Problem Solving and Gender: A Longitudinal Study

In this study, the differences between gender and general problem solving skills in programming knowledge were investigated. The types of programming knowledge were considered in three groups: conceptual, syntactic, and strategic knowledge. In the data analysis, latent growth model was used with longitudinal data. The results demonstrated the significant differences in favor of male students in prior conceptual and strategic knowledge. Male students were more increased their conceptual knowledge scores and strategic knowledge scores than female students during course of programming. Female students were more successful than male in initial status and in development of syntactic programming knowledge. According to other results, the higher level of the problem solving skill had student, the higher level of all knowledge of programming increased over time. (C) 2013 The Authors. Published by Elsevier Ltd

The Adaptation and Standardization of the Teacher Version of the Child Behavior Profile: Turkish Boys aged 7–12

This article reports on the adaptation and standardization of the teacher version of the Child Behavior Profile for boys aged 6–11 to Turkish boys aged 7–12. Data were obtained from 48 referred and 294 nonreferred boys. Comparison of referred and nonreferred samples showed significant differences on all behaviour problem scores, except the ‘Anxious’ scale. The median of the Cronbach internal consistency of the scale scores was 0.75.Publisher's Versio

Are boys more confident than girls? The role of calibration and students’ self-efficacy in programming tasks and computer science

Computer programming is regarded as a difficult subject at both school and university. There have been a vast amount of studies with a focus on identifying students' difficulties, common errors and misconceptions in programming, and on the development and design of instructional techniques that could potentially help students overcome these difficulties. Nevertheless, there are few studies that explore students' performance in programming under the prism of self-regulation theory. To this end, the current study considers girls' and boys' calibration and how it is related with their performance in programming, self-evaluation, and self-efficacy in computer science. Calibration is a measure of the accuracy with which people assess their confidence in their own performance. The results of our study suggest that boys feel significantly more efficacious in computer science than girls, as well as make significantly more accurate predictions (better calibrated) of their programming performance than girls. The implications of these findings for the current education practices are outlined and discussed

Using Learning Analytics To Develop Early-Warning System For At-Risk Students!

In the current study interaction data of students in an online learning setting was used to research whether the academic performance of students at the end of term could be predicted in the earlier weeks. The study was carried out with 76 second-year university students registered in a Computer Hardware course. The study aimed to answer two principle questions: which algorithms and features best predict the end of term academic performance of students by comparing different classification algorithms and pre-processing techniques and whether or not academic performance can be predicted in the earlier weeks using these features and the selected algorithm. The results of the study indicated that the kNN algorithm accurately predicted unsuccessful students at the end of term with a rate of 89%. When findings were examined regarding the analysis of data obtained in weeks 3, 6, 9, 12, and 14 to predict whether the end-of-term academic performance of students could be predicted in the earlier weeks, it was observed that students who were unsuccessful at the end of term could be predicted with a rate of 74% in as short as 3 weeks' time. The findings obtained from this study are important for the determination of features for early warning systems that can be developed for online learning systems and as indicators of student success. At the same time, it will aid researchers in the selection of algorithms and pre-processing techniques in the analysis of educational data.WoSScopu

Restructuring E-Learning With Ontologies

This paper examines the role of ontologies in the e-learning environments. A brief review of various ontologies is discussed in three areas: Learning design, learning content and learner profile. A new perspective for curriculum and instructional design is proposed.Wo