Education 4.0 — Fostering student's performance with machine learning methods

Ciolacu, Monica; Tehrani, Ali Fallah; Beer, Rick; Popp, Heribert

doi:10.1109/siitme.2017.8259941

Cited by 107 publications

(86 citation statements)

References 6 publications

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“…A second aspect, highlighted by Frutos-Pascual and Zapirain, is machine learning. Ciolacu, Tehrani, and Beer [22] argue that machine learning in education will be the fourth revolution towards utilizing student data for improving learning quality and for accurately predicting academic achievements. Techniques for machine learning in serious games include Bayesian models, neural net-works, case-based reasoning, support vector machines, and cluster analyses.…”

Section: Game Ai and Serious Game Aimentioning

confidence: 99%

Interactive Ant Colony Optimization to Support Adaptation in Serious Games

Kickmeier-Rust

Holzinger²

2019

IJSG

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The success of serious games usually depends on their capabilities to engage learners and to provide them with personalized gaming and learning experiences. Therefore, it is important to equip a game, as an autonomous computer system, with a certain level of understanding about individual learning trajectories and gaming processes. AI and machine learning technologies increasingly enter the field; these technologies often fail, however, since serious games either pose highly complex problems (combining gaming and learning process) or do not provide the extensive data bases that would be required. An interesting new direction is augmenting the strength of AI technologies with human intuition and human cognition. In the present paper, we investigated performance of the MAXMIN Ant System, a combinatorial optimization algorithm, with and without human interventions to the algorithmic procedure. As a testbed, we used a clone of the Travelling Salesman problem, the Travelling Snakesman game. We found some evidence that human interventions result in superior performance than the algorithm alone. The results are discussed regarding the applicability of this pathfinding algorithm in adaptive games, exemplified by Micro Learning Space adaptation systems.

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Section: Game Ai and Serious Game Aimentioning

confidence: 99%

Interactive Ant Colony Optimization to Support Adaptation in Serious Games

Kickmeier-Rust

Holzinger²

2019

IJSG

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“…With the purpose of enhancing safety of deaf people, the DADS alerts deaf people about alarming sounds and cautionary words by using a speech recognition engine and a sound recognition engine, which are implemented based on the Soundx algorithm and neural networks. Ciolacu et al [3] conduct analysis of estimating students' performance in examination with neural networks, support vector machine, decision trees, and cluster analysis. They also analyze the effectiveness of shaping the next generation's talent for Industry 4.0 skills with machine learning-related techniques.…”

Section: Related Workmentioning

confidence: 99%

UTCPredictor: An uncertainty‐aware novel teaching cases predictor

Zheng

et al. 2019

Comp Applic In Engineering

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Teaching cases are crucial for computer science (e.g., software testing) teaching. With the fast development of computer science, old and outdated teaching cases cannot meet the requirements of teaching. Therefore, teachers need to update teaching case repository continually and timely. However, teaching case development is an extremely time‐consuming work. Given today's complex and fast‐moving environment of computer science, teachers often feel blind about about what types of cases should be added for teaching. This paper presents UTCPredictor—an automated approach of predicting novel teaching cases from real production by identifying the most uncertain data, which are always with new features that reflect the latest developments and trends in the field. The implementation of UTCPredictor is based on the idea of interactive machine learning as well as several text mining techniques. To evaluate the effectiveness of UTCPredictor, we take bug report case building in software testing teaching as an example, using UTCPredictor to perform 10‐fold cross‐validation on an existing teaching case set. The performance in terms of indicators; Recall, Precision, and F1‐score, achieved three very competitive values—0.91, 0.94, and 0.85, respectively. We further evaluate the effectiveness of UTCPredictor through a user study and a questionnaire. The results are very positive; the user study indicates that educators can build a teaching case set from latest bug report repository by spending only 8.16%–18.11% time costs compared with traditional manual approach; the responses from 2,000 students for the questionnaire show that the teaching cases built with UTCPredictor are very popular among students.

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“…1 https://classic.csunplugged.org/wpcontent/uploads/2014/12/CSUnpluggedTeachers-portuguese-brazil-feb-2011.pdf atividades práticas e acesso a modulos de aprendizagem; (4) Adaptabilidade: atividades adaptadas conforme o conhecimento e a aprendizagem do estudante; (5) Programas de Suporte: métodos de análise para encontrar alunos com diculdades; (6) Sistema de Perguntas e Respostas Inteligentes: teletutores inteligentes para apoiar os alunos; e (7) E-Avaliação: correção de trabalho automaticamente através de sistema . Estas facetas podem contribuir para o protagonismo do estudante, dinamizar os processos de ensino e aprendizagem e colaborar para a formação de competências necessárias para a vida no Século XXI [7].…”

Section: Introductionunclassified

“…faceta Personalização (criação de material didático preparado para atender diferentes tipos de aprendizagem) proposta por [7]. A partir do conceito de Computação Desplugada, desenvolveu-se um labirinto impresso com a finalidade de apoiar os estudantes no desenvolvimento de competências e habilidades do Século XXI.…”

Section: Introductionunclassified

Educação 4.0: um Estudo de Caso com Atividades de Computação Desplugada na Amazônia Brasileira

Silva¹,

Sobrinho²,

Valentim³

2020

Anais Do XI Computer on the Beach - COTB '20

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This case study aimed to cultivate 21st Century competences and skills related to Education 4.0, such as problem-solving, collaboration and autonomy. To this end, unplugged computing activities were performed with 25 students from four rural schools located in Tapajós-Arapiúns (Brazilian Amazon). As part of the methodology, a maze based on Google's Blockly Games tool was produced, inspired by hopscotch, a popular joke among children. After this case study, a qualitative analysis was performed, and it indicates that Unplugged Computing activities instigate students' skills and competences, as autonomy, communication and teamwork. Besides, this methodology proves to be useful for disseminating Education 4.0 concepts, due to its ease of application and low cost, especially in distant places with few resources.

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Education 4.0 — Fostering student's performance with machine learning methods

Cited by 107 publications

References 6 publications

Interactive Ant Colony Optimization to Support Adaptation in Serious Games

Interactive Ant Colony Optimization to Support Adaptation in Serious Games

UTCPredictor: An uncertainty‐aware novel teaching cases predictor

Educação 4.0: um Estudo de Caso com Atividades de Computação Desplugada na Amazônia Brasileira

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