Search

In this paper, a new approach for the formation of four‐member collaborative learning groups is presented. Group formation is presented by the mathematical optimization problem. Based on the proposed approach and the variable neighbourhood search (VNS) algorithm, the application that solves the presented problem and provides the appropriate division into groups is created. The proposed approach considers the scores of a pretest, interpersonal relationships, and prosocial behaviour/openness skill of students. In order to validate our approach, an experiment was designed with 108 first‐year university students of Belgrade Business School—Higher Educational Institution for Applied Studies. Experimental and control groups were divided into four‐member groups. The experimental group was divided by using the proposed method and the control group by student selection and random selection. Multilevel analysis is used to determine whether there is a significant difference in learning outcomes between the two groups. The experimental results showed that students from the experimental group achieved significantly higher success than the students from the control group. In addition, computational results obtained with the proposed VNS algorithms are compared and verified with the results obtained by random (Monte Carlo) method. Lay Description: What is currently known about the subject matter of this paper: Group formation have great influence on the success of collaborative learning.Consideration of multiple variables for group formation can be very demanding.Previous computer‐based methods for group formation do not use VNS. What this paper adds: In this paper, new metaheuristic approach for group formation is proposed.The proposed approach considers the scores of a pretest.The proposed approach considers interpersonal relationships.The proposed approach considers prosocial behaviour/openness skill of students. The implications of study findings for practitioners: The proposed approach have positive impact on students' academic performance.This approach can divide a large number of students into smaller groups based on multiple variables. [ABSTRACT FROM AUTHOR]

Background: The idea of computational thinking is underpinned by the belief that anyone can learn and use the underlying concepts of computer science to solve everyday problems. However, most studies on the topic have investigated the development of computational thinking through programming activities, which are cognitively demanding. There is a dearth of evidence on how computational thinking augments everyday problem solving when it is decontextualized from programming. Objectives: In this study, we examined how computational thinking, when untangled from the haze of programming, is demonstrated in everyday problem solving, and investigated the features of such solvable problems. Methods: Using a multiple case study approach, we tracked how seven university students used computational thinking to solve the everyday problem of a route planning task as part of an 8‐week‐long Python programming course. Results and Conclusions: Computational thinking practices are latent in everyday problems, and intentionally structuring everyday problems is valuable for discovering the applicability of computational thinking. Decomposition and abstraction are prominent computational thinking components used to simplify everyday problem solving. Implications: It is important to strike a balance between the correctness of algorithms and simplification of the process of everyday problem solving. Lay Description: What is already known about this topic: Computational thinking is a problem solving skill.Computational thinking is different from programming.There is dearth of evidence on how computational thinking augments everyday problem solving. What this paper adds: Computational thinking practices are latent in everyday problem solving.Intentional restructuring of problems is required to uncover how computational thinking applies in solving them. Implications for practices: Decomposition and abstraction are critical in solving everyday problems.Computational thinking should be taught explicitly through everyday problems.Finding balance between the correctness of algorithm and simplification of the problem solving process is important. [ABSTRACT FROM AUTHOR]

Multiple external representations (MERs) play an important role in the learning field of mathematics. Whereas the cognitive theory of multimedia learning and the integrative text and picture comprehension model assume that the heterogeneous combination of symbolic and analogous representations fosters learning; the design, functions, and tasks framework holds that learning benefits depend on the specific functions of MERs. The current paper describes a conceptual replication study of one of the few studies comparing single representations, heterogeneous, and homogeneous MERs in the context of mathematics learning. In a balanced incomplete block design, the participants were provided single representations (a graphic, text, or formula) or a heterogeneous (e.g., text + graphic) or homogeneous (text + formula) combination of these to solve linear system of equations problems. In accordance with previous research, performance was superior in conditions providing MERs compared to single‐representation conditions. Moreover, heterogeneous MERs led to time savings over homogeneous MERs which triggered an increase in cognitive load. Contrary to previous research, text was the least fixated representation whereas the graphical representation proved to be most beneficial. With regard to practical implications, experts should be fostered through more challenging homogeneous MERs whereas novices should be supported through the accessible graphic contained in heterogeneous MERs. Lay Description: What is already known about this topic: Providing multiple instead of single representations can foster mathematics learning.However, some combinations of representations hamper the performance of novice learners.The classical multimedia view explains the positive effect of combining text with picture.Recent research suggests the combination of multiple symbolic representations (text and formula). What this paper adds: The study replicated the effect of multiple symbolic representations for equation solving.In contrast to former research, each representation (text, formula, and graphic) was functionally equivalent.Tasks with differently coded representations were solved faster and with less mental effort.Gaze behavior indicated that students mostly used the graphical representation. Implications for practice and/or policy: Beginners should be supported with a graphic when solving equations.Experts should be challenged with a combination of symbolic representations to benefit the most. [ABSTRACT FROM AUTHOR]

This article addresses the problem of detecting crisis‐related messages on social media, in order to improve the situational awareness of emergency services. Previous work focused on developing machine‐learning classifiers restricted to specific disasters, such as storms or wildfires. We investigate for the first time methods to detect such messages where the type of the crisis is not known in advance, that is, the data are highly heterogeneous. Data heterogeneity causes significant difficulties for learning algorithms to generalize and accurately label incoming data. Our main contributions are as follows. First, we evaluate the extent of this problem in the context of disaster management, finding that the performance of traditional learners drops by up to 40% when trained and tested on heterogeneous data vis‐á‐vis homogeneous data. Then, in order to overcome data heterogeneity, we propose a new ensemble learning method, and found this to perform on a par with the Gradient Boosting and AdaBoost ensemble learners. The methods are studied on a benchmark data set comprising 26 disaster events and four classification problems: detection of relevant messages, informative messages, eyewitness reports, and topical classification of messages. Finally, in a case study, we evaluate the proposed methods on a real‐world data set to assess its practical value. [ABSTRACT FROM AUTHOR]

Past research has shown that student problem‐solving skills may be used to determine student final exam performance. This study reports on the relationship between student perceived problem‐solving skills and academic performance in introductory programming, in formative and summative programming assessment tasks. We found that the more effective problem solvers achieved better final exam scores. There was no significant difference in formative assessment performances between effective and poor problem solvers. It is also possible to categorize students on the basis of problem‐solving skills, in order to exploit opportunities to improve learning around constructivist learning theory. Finally, our study identified transferability skills and the study may be extended to identify the impact of problem solving transfer skills on student problem solving for programming. Lay Description: Literature review: Problem‐solving skill (PSS) is a required skill for all, including students to analyse and find effective solutions to solve problems.PSS can be a significant predictor for course grade and to identify student study habits and attitudes that important to academic performance.It seems PSS and learning programming are interrelated but however are not yet deeply examined or little research has been conducted around the impact of student general PSS on novice assessment performance in programming. Our findings: The outcome of the analysis reported in this study was derived from a range of statistical methods, including mean with confidence interval, correlation coefficient, Kruskal–Wallis and Bonferroni post hoc correction methods.There is a significant difference in student performance in summative assessment tasks with effective PSS and students with poor PSS. However, our research findings on relationship between PSS and formative assessment tasks led us to surmise that in general PSS may not be considered as a significant factor to predict student performance in formative assessment tasks, although this should be analysed further to support our conclusion.This study results suggest that students with poor PSS may have issues with problem‐solving transferable skills. In addition, students who lack PSS would see the problems as a threat or difficult task to solve and have low confidence in their problem‐solving capabilities. Implications for educational practice and/or policy: The findings reveal that it is quite possible to categorize students on the basis of PSS, in order to exploit opportunities to improve student learning on the basis of constructivist learning theory.Assessing student PSS levels may help instructors to develop instructional interventions to improve student academic self‐efficacy in learning programming. However, attention should be paid to instructional methodologies and formative assessments that are not aligned with summative assessments, in order to avoid the problem solving skills transferability shortcoming.In addition, teachers are expected to deliver teaching and learning activities and iterative formative feedback and guidance to support students to construct knowledge.However, the challenge for educators is to develop diverse assessment methods for introductory level courses to reduce the summative assessment performance gap between students with good and poor PSS. [ABSTRACT FROM AUTHOR]