Completeness based classification algorithm: a novel approach for educational semantic data completeness assessment

Purpose The purpose of this paper is to reveal the smart collaborative learning service. This concept aims to build teams of learners based on the complementarity of their skills, allowing flexible participation and offering interdisciplinary collaboration opportunities for all the learners. The success of this environment is related to predict efficient collaboration between the different teammates, allowing a smartly sharing knowledge in the Smart University environment. Design/methodology/approach A random forest (RF) approach is proposed, which is based on semantic modelization of the learner and the problem-solving allowing multidisciplinary collaboration, and heuristic completeness processing to build complementary teams. To achieve that, this paper established a Konstanz Information Miner workflow that integrates the main steps for building and evaluating the RF classifier, this workflow is divided into: extracting knowledge from the smart collaborative learning ontology, calculating the completeness using a novel heuristic and building the RF classifier. Findings The smart collaborative learning service enables efficient collaboration and democratized sharing of knowledge between learners, by using a semantic support decision support system. This service solves a frequent issue related to the composition of learning groups to serve pedagogical perspectives. Originality/value The present study harmonizes the integration of ontology, a new heuristic processing and supervised machine learning algorithm aiming at building an intelligent collaborative learning service that includes a qualified classifier of complementary teams of learners.

[1]  Syed Akhter Hossain,et al.  Ontology-Based Information Retrieval System for University: Methods and Reasoning , 2019 .

[2]  Holger H. Hoos,et al.  A survey on semi-supervised learning , 2019, Machine Learning.

[3]  Miguel Ángel Conde González,et al.  Predicting teamwork group assessment using log data-based learning analytics , 2018, Comput. Hum. Behav..

[4]  P. Deepa Shenoy,et al.  Aspect term extraction for sentiment analysis in large movie reviews using Gini Index feature selection method and SVM classifier , 2016, World Wide Web.

[5]  Mounir Ben Ayed,et al.  Teamwork construction in E-learning system: A systematic literature review , 2016, 2016 15th International Conference on Information Technology Based Higher Education and Training (ITHET).

[6]  H. Andrés Neyem,et al.  MyMOOCSpace: Mobile cloud‐based system tool to improve collaboration and preparation of group assessments in traditional engineering courses in higher education , 2018, Comput. Appl. Eng. Educ..

[7]  Thomas G. Dietterich.  What is machine learning? , 2020, Archives of Disease in Childhood.

[8]  Ajith Abraham,et al.  Semantic assessment of smart healthcare ontology , 2020, Int. J. Web Inf. Syst..

[9]  Olusegun Folorunso,et al.  An Ontology-based Knowledge Acquisition Model for Software Anomalies Systems , 2020, 2020 International Conference in Mathematics, Computer Engineering and Computer Science (ICMCECS).

[10]  Benjamin Hirsch,et al.  Quantitative approach to collaborative learning: performance prediction, individual assessment, and group composition , 2016, International Journal of Computer-Supported Collaborative Learning.

[11]  Shihong Huang,et al.  Using the random forest classifier to assess and predict student learning of Software Engineering Teamwork , 2016, 2016 IEEE Frontiers in Education Conference (FIE).

[12]  Cristóbal Romero,et al.  Educational data mining and learning analytics: An updated survey , 2020, WIREs Data Mining Knowl. Discov..

[13]  Nabil Hmina,et al.  Smart university: SOC-based study , 2018, SCA.

[14]  Ion Smeureanu,et al.  Using Ontologies in Cybersecurity Field , 2017 .

[15]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[16]  Demetrio Arturo Ovalle Carranza,et al.  A genetic algorithm approach for group formation in collaborative learning considering multiple student characteristics , 2012, Comput. Educ..

[17]  Seiji Isotani,et al.  Group Formation Algorithms in Collaborative Learning Contexts: A Systematic Mapping of the Literature , 2014, CRIWG.

[18]  Claudiu Vinte,et al.  Upon a Home Assistant Solution Based on Raspberry Pi Platform , 2017 .

[19]  Carlos Angel Iglesias,et al.  A semantic similarity-based perspective of affect lexicons for sentiment analysis , 2019, Knowl. Based Syst..

[20]  Mahesh Pal,et al.  Random forest classifier for remote sensing classification , 2005 .

[21]  Hui Xiong,et al.  A Comprehensive Survey on Transfer Learning , 2019, Proceedings of the IEEE.

[22]  M. Herrera-Pavo,et al.  Collaborative learning for virtual higher education , 2021 .

[23]  Sonali Agarwal,et al.  Data Mining in Education: Data Classification and Decision Tree Approach , 2012 .

[24]  Wenlong Fu,et al.  Model-based reinforcement learning: A survey , 2018 .