Group Formation Techniques in Computer-Supported Collaborative Learning: A Systematic Literature Review

Group formation is an essential process for group development lifecycle. It has been a growing concern to many researchers to be applied automatically in collaborative learning contexts. Forming a group is an atomic process that is affected by various factors. These factors differ depending on the group members characteristics, the context of the grouping process and the techniques used to form the group(s). This paper surveys the recently published work in group formation process providing a systematic literature review in which 30 relevant studies were analyzed. The findings of this review propose two taxonomies. The first one is for the attributes of group formation while the second is for the grouping techniques. Furthermore, we present the main findings and highlight the limitations of existing approaches in computer supported collaborative learning environment. We suggest some potential directions for future research with group formation process in both theoretical and practical aspects. In addition, We emphasize other improvements that may be inter-related with other computing areas such as cloud computing and mobility

Adapting Collaborative Learning Tools to Support Group Peer Mentorship

Group peer mentorship is a relatively new addition to the area of collaborative learning. We see an untapped potential in supporting this model of mentorship with the existing collaborative learning tools like peer review and wiki. Therefore, we proposed to use a modified peer review system and a modified wiki system. From our preliminary studies using both peer review and wiki systems, we found that participants preferred the peer-review system to the wiki system in supporting them for mentorship. Therefore, this dissertation specifically addresses how to adapt the peer review system to support group peer mentorship. We proposed a modified peer review system, which comprises seven stages – initial submission of the first draft of the paper by the author, the review of author’s paper by peer reviewers, release of review feedback to the author, back-evaluation of their reviews by the authors, modification of the paper by the author, submission of the final paper and the final stage where both authors and reviewers provide an evaluation of the peer review process with respect to their learning, their perception of the helpfulness of the process, and their satisfaction with the process. We also proposed to use our group matching algorithm, based on some constraints and the principles of the Hungarian algorithm, to achieve a diversified grouping of peers for each peer review session. With these, we conducted six peer review studies with the graduate and undergraduate students at the University of Saskatchewan and teachers in Chile. This dissertation reports on the findings from these studies. We found that peer review, with some modifications, is a good tool to facilitate group peer mentorship. An evaluation of the performance of our group matching algorithm showed an improvement over three other algorithms, with respect to three metrics – knowledge gain of peers, time and space consumption of the algorithm. Finally, this dissertation also shows that wiki has the potential to support group peer mentorship, but needs further research

Open-ended Search through Minimal Criterion Coevolution

Search processes guided by objectives are ubiquitous in machine learning. They iteratively reward artifacts based on their proximity to an optimization target, and terminate upon solution space convergence. Some recent studies take a different approach, capitalizing on the disconnect between mainstream methods in artificial intelligence and the field\u27s biological inspirations. Natural evolution has an unparalleled propensity for generating well-adapted artifacts, but these artifacts are decidedly non-convergent. This new class of non-objective algorithms induce a divergent search by rewarding solutions according to their novelty with respect to prior discoveries. While the diversity of resulting innovations exhibit marked parallels to natural evolution, the methods by which search is driven remain unnatural. In particular, nature has no need to characterize and enforce novelty; rather, it is guided by a single, simple constraint: survive long enough to reproduce. The key insight is that such a constraint, called the minimal criterion, can be harnessed in a coevolutionary context where two populations interact, finding novel ways to satisfy their reproductive constraint with respect to each other. Among the contributions of this dissertation, this approach, called minimal criterion coevolution (MCC), is the primary (1). MCC is initially demonstrated in a maze domain (2) where it evolves increasingly complex mazes and solutions. An enhancement to the initial domain (3) is then introduced, allowing mazes to expand unboundedly and validating MCC\u27s propensity for open-ended discovery. A more natural method of diversity preservation through resource limitation (4) is introduced and shown to maintain population diversity without comparing genetic distance. Finally, MCC is demonstrated in an evolutionary robotics domain (5) where it coevolves increasingly complex bodies with brain controllers to achieve principled locomotion. The overall benefit of these contributions is a novel, general, algorithmic framework for the continual production of open-ended dynamics without the need for a characterization of behavioral novelty

Benchmarking Continuous Dynamic Optimization: Survey and Generalized Test Suite

Dynamic changes are an important and inescapable aspect of many real-world optimization problems. Designing algorithms to find and track desirable solutions while facing challenges of dynamic optimization problems is an active research topic in the field of swarm and evolutionary computation. To evaluate and compare the performance of algorithms, it is imperative to use a suitable benchmark that generates problem instances with different controllable characteristics. In this paper, we give a comprehensive review of existing benchmarks and investigate their shortcomings in capturing different problem features. We then propose a highly configurable benchmark suite, the generalized moving peaks benchmark, capable of generating problem instances whose components have a variety of properties such as different levels of ill-conditioning, variable interactions, shape, and complexity. Moreover, components generated by the proposed benchmark can be highly dynamic with respect to the gradients, heights, optimum locations, condition numbers, shapes, complexities, and variable interactions. Finally, several well-known optimizers and dynamic optimization algorithms are chosen to solve generated problems by the proposed benchmark. The experimental results show the poor performance of the existing methods in facing new challenges posed by the addition of new properties

Effects of Cooperative Learning on Motivation, Learning Strategy Utilization, and Grammar Achievement of English Language Learners in Taiwan

To examine the effects of cooperative learning on EFL students in Taiwan, a 12-week quasi-experimental pretest-posttest comparison group research study was designed. Two college classes (42 students each) in Taiwan participated in the study, one receiving grammar instruction through cooperative learning and the other through whole-class teaching. Three specific research questions guided the study. The first looked at effects of cooperative learning on motivation, the second on out-of-class strategy use, and the third on grammar achievement. Additional exploratory questions examined these results across subgroups within each class as well as the relationships between the dependent variables. Data were collected via learners\u27 pretest and posttest scores on the dependent variables. The data were analyzed with MANCOVAs, one- and two-way ANCOVAs, simple effects, and Pearson correlations. Cooperative learning was found to have large positive effects on motivation and strategy use, and medium-to-large positive effects on grammar achievement. Overall, the findings indicated a consistent pattern in favor of cooperative learning over whole-class instruction in teaching the Taiwanese learners English grammar. The results of the exploratory questions indicated that cooperative learning facilitated motivation and strategy use of learners across all subgroups, but more so with those performing at higher and lower levels. Grammar achievement of learners at higher and lower levels was affected positively. Additional analyses also indicated cooperative learning positively affected learning at higher cognitive levels. Implications for future research and for curriculum and instruction are addressed