Development of online learning groups based on MBTI learning style and fuzzy algorithm

Group development is an initial step and an important influence on learning collaborative problem solving (CPS) based on the digital learning environment (DLE). Group development based on the Myers-Briggs types indicators (MBTI) rule proved successful for the educational and industrial environment. The MBTI ideal group rules are reached when a group leader has the highest level of leadership and compatibility between group members. The level of leadership and suitability of group members is determined based on the MBTI learning style (LS). Problems arise when the population of MBTI LS with the highest level of leadership is over. This will lead to dual leadership problems and have an impact on group disharmony. This study proposes an intelligent agent software for the development of the ideal group of MBTI, using the Fuzzy algorithm. The intelligent agent was developed on the SKACI platform. SKACI is a DLE for CPS learning. Fuzzy algorithm for solving dual leadership problems in a group. Fuzzy algorithm is used to increase the population of MBTI LS to 3 levels, namely low, medium and high. Increasing the population of MBTI LS can increase the probability of forming an ideal group of MBTI. Intelligent agents are tested based on a quantitative analysis between experimental classes (applying intelligent agents), and control classes (without intelligent agents). Experiment results show an increase in performance and productivity is better in the experimental class than in the control class. It was concluded that the development of intelligent agents had a positive impact on group development based on the MBTI LS

ピアアセスメントにおける項目反応理論を用いたグループ構成最適化

近年，社会構成主義に基づく学習評価法としてピアアセスメントが注目されている．一般に，MOOCsのように学習者数が多い場合のピアアセスメントは，評価の負担を軽減するために学習者を複数のグループに分割してグループ内のメンバ同士で行うことが多い．しかし，この場合，学習者の能力測定精度がグループ構成の仕方に依存する問題が残る．この問題を解決するために，本研究では，項目反応理論を用いて，学習者の能力測定精度を最大化するようにグループを構成する手法を提案する．しかし，実験の結果，ランダムにグループを構成した場合と比べ，提案手法が必ずしも高い能力測定精度を示すとは限らないことが明らかとなった．そこで，本研究では，グループ内の学習者同士でのみ評価を行うという制約を緩和し，各学習者に対して少数のグループ外評価者を割り当てる外部評価者選択手法を提案する．シミュレーションと被験者実験から，提案手法を用いて数名の外部評価者を追加することで，グループ内の学習者のみによる評価に比べ，能力測定精度が改善されることが確認された．As an assessment method based on social constructivism, peer assessment has attracted much attention in recent years. When learners increase as in MOOCs, peer assessment is often conducted by dividing learners into groups. However, in this case, the accuracy of peer assessment depends on a way of forming groups. To optimize the accuracy, this study develops a group optimization method using item response theory. However, experimental results show that the method cannot sufficiently improve the accuracy compared to random groups. Therefore, the study further proposes an external rater selection method to assign a few appropriate outside-group raters to each learner. Experimental results demonstrate that the proposed method can sufficiently improve the accuracy

PEMBENTUKAN KELOMPOK COLLABORATIVE PROBLEM SOLVING BERDASARKAN PERSONALITY TRAITS MENGGUNAKAN ALGORITMA GENETIKA

Kemampuan untuk bekerja dalam tim dan mampu berkoordinasi dengan tim sangatlah penting untuk kemajuan pengetahuan dan keberhasilan pekerjaan apapun. Oleh sebab itu, salah satu aspek penting dalam pembelajaran collaborative problem solving (CPS) yaitu membentuk kelompok. Dalam membentuk kelompok belajar CPS dibutuhkan komposisi kelompok yang heterogen. Tujuan dari penelitian ini adalah membentuk, mengukur kinerja kelompok, dan mengukur nilai collaborative performance kelompok. Pada penelitian ini, pengelompokan dibentuk dari atribut personality traits yang dimana harus terdapat sifat yang berbeda dalam satu kelompok dengan metode algoritma genetika. Dalam memilih atribut penulis mencocokannya dengan keterampilan yang harus dimiliki di pendidikan abad 21. Dalam proses pengelompokan, sistem menghasilkan rata-rata nilai fitness 0.9778 dan menghasilkan nilai heterogenitas tertinggi yaitu 4. Hal ini membuktikan bahwa kelompok yang dibentuk oleh algoritma genetika tersebut benar-benar heterogen. Setelah dilakukan pembelajaran CPS selama 3 kali pertemuan, mendapatkan beberapa hasil yaitu terjadi peningkatan di hasil knowledge setelah dilakukan tugas kelompok dengan nilai rata-rata tertinggi 77.08 dan terjadi peningkatan collaborative performance kelompok untuk setiap pertemuannya dengan nilai tertinggi dengan rata-rata 3.65 yang diraih oleh kelompok 7 dan 8. Hal ini menunjukan bahwa pembentukan kelompok belajar berdasarkan personality traits dengan algoritma genetika mempengaruhi nilai yang diraih siswa. The ability to work in teams and be able to coordinate with team is very important for the advancement of knowledge and the success of any job. Therefore, one important aspect in collaborative problem solving (CPS) learning is forming groups. In forming a CPS study group a heterogeneous group composition is needed. The purpose of this study is to form, measure group performance, and measure the value of collaborative group performance. In this study, grouping is formed from the personality traits attribute which must have different traits in one group using the genetic algorithm method. In choosing the attribute the author matches it with the skills that must be possessed in 21st century education. In the grouping process, the system produces an average fitness value of 0.9778 and produces the highest heterogeneity value of 4. This proves that the groups formed by the genetic algorithm are truly heterogeneous . After doing CPS learning for 3 meetings, getting some results, there was an increase in knowledge after group assignments with the highest average score of 77.08 and an increase in collaborative performance groups for each meeting with the highest score with an average of 3.65 achieved by the group 7 and 8. This shows that the formation of study groups based on personality traits with genetic algorithms affects the scores achieved by students

Learning log-based automatic group formation: system design and classroom implementation study

Collaborative learning in the form of group work is becoming increasingly significant in education since interpersonal skills count in modern society. However, teachers often get overwhelmed by the logistics involved in conducting any group work. Valid support for executing and managing such activities in a timely and informed manner becomes imperative. This research introduces an intelligent system focusing on group formation which consists of a parameter setting module and the group member visualization panel where the results of the created group are shown to the user and can be graded. The system supports teachers by applying algorithms to actual learning log data thereby simplifying the group formation process and saving time for them. A pilot study in a primary school mathematics class proved to have a positive effect on students’ engagement and affections while participating in group activities based on the system-generated groups, thus providing empirical evidence to the practice of Computer-Supported Collaborative Learning (CSCL) systems

Enhance Classroom Preparation for Flipped Classroom using AI and Analytics

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Learners’ frequent pattern discovering in a dynamic collaborative learning environment designed based on game theory

Background and Objectives:In any educational system, the optimal output of educational approach is of particular importance. Therefore, considering the personality characters of individuals and providing educational services in accordance with their characteristics are effective factors in learning and educational efficiency improvement. Analyzing the data related to learner’s behavior in an educational environment and implicitly discovering the learner’s personality based on their behavior is a well-noticed study in recent years. Over the last few years, using learners’ information such as number of friends, the level of activities in educational forum, writing style of learner, study duration, the difficulty of solved problem, the difficulty of presented example by learners, number of clicks, number of signs in sentences, the time spent doing homework are items that has been used to personal characteristic identification. This study is aimed at using teammates’ changing / not changing data in order to learners’ personality identification. For this purpose the teammates’ changing/ not changing data extracted from a dynamic collaborative learning environment that allows leaners to change their teammate during the different sessions of learning, are used. The design and implementation of mentioned dynamic collaborative learning environment is based on game theory. Game theory provides mathematical models of conflict and collaboration between intelligent rational decision-makers. Methods: In this paper, we collect teammates’ changing/not changing information of 119 randomly selected computer engineering students from a game theoretical dynamic collaborative learning environment. At the next step, using frequent pattern mining, as a tools of data mining, some aspects of the neo big 5 personality traits of learners are identified. In this survey, in order to evaluate the results, the extracted patterns from frequent pattern mining are compared with the neo big 5 personality questionnaire that have been filled by learners. In another part of research, using the Laplace’s rule of succession, valuable predictions were made about the probability of teammate’s changing of learners during the learning process. Findings: In this study, using frequent pattern mining in learners’ behaviour, we identified some neo big 5 personality traits such as those in the first (neuroticism), second (extraversion), and third (openness to experience) dimensions, with an acceptable support value. The results of this part of research can be used in any adaptive learning environment that adapt learning process for individual learners with different personality. At the next step of our study, we predicted the probability of the teammate changing in the sessions after. At this step, we had a prediction accuracy of up to 67.44%. Using the results of this part, teammate suggestion can be made to learner based on likelihood of their teammates’ changing. That is, higher teammate changing probability, more appropriate teammate suggestion to learner. Conclusion: The results of the present study can be used in any adaptive system that requires predicting group change behaviour or identifying personality dimensions based on behaviour.   ===================================================================================== COPYRIGHTS  ©2020 The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, as long as the original authors and source are cited. No permission is required from the authors or the publishers.  ====================================================================================

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

Changing the focus: worker-centric optimization in human-in-the-loop computations

A myriad of emerging applications from simple to complex ones involve human cognizance in the computation loop. Using the wisdom of human workers, researchers have solved a variety of problems, termed as “micro-tasks” such as, captcha recognition, sentiment analysis, image categorization, query processing, as well as “complex tasks” that are often collaborative, such as, classifying craters on planetary surfaces, discovering new galaxies (Galaxyzoo), performing text translation. The current view of “humans-in-the-loop” tends to see humans as machines, robots, or low-level agents used or exploited in the service of broader computation goals. This dissertation is developed to shift the focus back to humans, and study different data analytics problems, by recognizing characteristics of the human workers, and how to incorporate those in a principled fashion inside the computation loop. The first contribution of this dissertation is to propose an optimization framework and a real world system to personalize worker’s behavior by developing a worker model and using that to better understand and estimate task completion time. The framework judiciously frames questions and solicits worker feedback on those to update the worker model. Next, improving workers skills through peer interaction during collaborative task completion is studied. A suite of optimization problems are identified in that context considering collaborativeness between the members as it plays a major role in peer learning. Finally, “diversified” sequence of work sessions for human workers is designed to improve worker satisfaction and engagement while completing tasks

Reflective agents for personalisation in collaborative games

The collaborative aspect of games has been shown to potentially increase player performance and engagement over time. However, collaborating players need to perform well for the team as a whole to benefit and thus teams often end up performing no better than a strong player would have performed individually. Personalisation offers a means for improving overall performance and engagement, but in collaborative games, personalisation is seldom implemented, and when it is, it is overwhelmingly passive such that the player is not guided to goal states and the effectiveness of the personalisation is not evaluated and adapted accordingly. In this paper, we propose and apply the use of reflective agents to personalisation (‘reflective personalisation’) in collaborative gaming for individual players within collaborative teams via a combination of individual player and team profiling in order to improve player and thus team performance and engagement. The reflective agents self-evaluate, dynamically adapting their personalisation techniques to most effectively guide players towards specific goal states, match players and form teams. We incorporate this agent-based approach within a microservices architecture, which itself is a set of collaborating services, to facilitate a scalable and portable approach that enables both player and team profiles to persist across multiple games. An experiment involving 90 players over a two-month period was used to comparatively assess three versions of a collaborative game that implemented reflective, guided, and passive personalisation for individual players within teams. Our results suggest that the proposed reflective personalisation approach improves team player performance and engagement within collaborative games over guided or passive personalisation approaches, but that it is especially effective for improving engagement

“The Illusion of Collaboration”: An Integrated Examination of the Antecedents, Processes, and Consequences of Online Group Work

Computer-supported collaborative learning (CSCL) presents postsecondary educators with a conundrum: how to design and support small-group activities without stifling deep and meaningful learning. The literature indicates that students are not consistently practicing higher-order cognitive activities, educators are not reliably designing or facilitating them, and/or researchers are not locating or identifying them where they are occurring. The aim of this dissertation is to explore these deficits by identifying the antecedent conditions that most affect collaboration. Specifically, I answer the question, how do learner’s prior knowledge, characteristics, and experiences manifest in their collaborative processes. Addressing a gap in the literature, this study employs distance ethnography to assess at a fine-grain level the social and cognitive interactions of a trio of collaborators in a natural setting—an object-oriented, small-group project in an online writing course. The results reveal several ways that learner dispositions and prior knowledge manifest as barriers to productive interactions, including tendencies toward indirect and unidirectional communication; siloed workspaces and individual orientations to group assignments; unequal coordination work; and the preservation of individual autonomy to the detriment of group knowledge objects. The study has pedagogical and theoretical implications related to the theory of transactional distance (TTD) and collaborative cognitive load theory (CCLT) and pedagogical and methodological implications for the integration of reflective-practitioner journals