PREDICTING THE LEARNING PATH TO LEARNER’S OPTIMUM COMPREHENSION
The essence of learning is for the learner to attain a significant level of comprehension after the learning process is completed. The quest to achieve this singular purpose has led to the introduction of several learning techniques in the conventional learning environment, such as asking question...
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| Format: | Article |
| Language: | English |
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Institute for Digitalisation of Education of the NAES of Ukraine
2024-04-01
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| Series: | Інформаційні технології і засоби навчання |
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| Online Access: | https://journal.iitta.gov.ua/index.php/itlt/article/view/5530 |
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| author | Ifeanyi Isaiah Achi Chukwuemeka Odi Agwu Christopher Chizoba Nnamene Sylvester C. Aniobi Ifebude Barnabas C. Kelechi Christian Oketa Godson Kenechukwu Ezeh John Otozi Ugah |
| author_facet | Ifeanyi Isaiah Achi Chukwuemeka Odi Agwu Christopher Chizoba Nnamene Sylvester C. Aniobi Ifebude Barnabas C. Kelechi Christian Oketa Godson Kenechukwu Ezeh John Otozi Ugah |
| author_sort | Ifeanyi Isaiah Achi |
| collection | DOAJ |
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The essence of learning is for the learner to attain a significant level of comprehension after the learning process is completed. The quest to achieve this singular purpose has led to the introduction of several learning techniques in the conventional learning environment, such as asking questions and conducting test after class, just to mention a few. Additionally, technology has been introduced in learning. Even with technological advancements, the learning experience still faces the challenge of learners not attaining the optimum comprehension state after the learning process. This is due to the present systems' inability to model the learner to determine the best methods for achieving maximum comprehension. Hence, this research paper focuses on deriving an improved mathematical model for predicting the learning path to a learner’s optimum comprehension. The paper presented three learning instructional media (learning paths); textual, audio and a hybrid of audio and video, which this research uses in modelling the learner. This is to enable the improved system predict the best learning path to optimum comprehension for learners. This research paper adopted Reinforcement Learning and the Markov decision process, specifically the Markov Chain approach, in developing an improved model for prediction. The evaluation of this research involved brainstorming on the Bellman’s equation with the aid of the Markov Chain transition state framework, resulting in an improved mean value function of 71.7. This indicates an enhanced comprehension state for the learning students compared to the existing mean value function of 46.0. The results obtained from this research clearly demonstrate that the improved model was able to predict and assign the best learning path to achieve optimum comprehension state for learners.
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| format | Article |
| id | doaj-art-e21d4d65ebb54456824ea74e143482e1 |
| institution | Kabale University |
| issn | 2076-8184 |
| language | English |
| publishDate | 2024-04-01 |
| publisher | Institute for Digitalisation of Education of the NAES of Ukraine |
| record_format | Article |
| series | Інформаційні технології і засоби навчання |
| spelling | doaj-art-e21d4d65ebb54456824ea74e143482e12025-02-09T08:37:18ZengInstitute for Digitalisation of Education of the NAES of UkraineІнформаційні технології і засоби навчання2076-81842024-04-01100210.33407/itlt.v100i2.5530PREDICTING THE LEARNING PATH TO LEARNER’S OPTIMUM COMPREHENSIONIfeanyi Isaiah Achi0https://orcid.org/0000-0003-4557-2929Chukwuemeka Odi Agwu1Christopher Chizoba Nnamene2Sylvester C. Aniobi3Ifebude Barnabas C.4Kelechi Christian Oketa5Godson Kenechukwu Ezeh6John Otozi Ugah7Alex Ekwueme Federal University Ndufu Alike IkwoEbonyi State UniversityAlex Ekwueme Federal University Ndufu Alike IkwoNational Open University EnuguAlex Ekwueme Federal University Ndufu Alike IkwoAlex Ekwueme Federal University Ndufu Alike IkwoOur Saviour Institute of Agriculture Scienceand TechnologyAlex Ekwueme Federal University Ndufu Alike Ikwo The essence of learning is for the learner to attain a significant level of comprehension after the learning process is completed. The quest to achieve this singular purpose has led to the introduction of several learning techniques in the conventional learning environment, such as asking questions and conducting test after class, just to mention a few. Additionally, technology has been introduced in learning. Even with technological advancements, the learning experience still faces the challenge of learners not attaining the optimum comprehension state after the learning process. This is due to the present systems' inability to model the learner to determine the best methods for achieving maximum comprehension. Hence, this research paper focuses on deriving an improved mathematical model for predicting the learning path to a learner’s optimum comprehension. The paper presented three learning instructional media (learning paths); textual, audio and a hybrid of audio and video, which this research uses in modelling the learner. This is to enable the improved system predict the best learning path to optimum comprehension for learners. This research paper adopted Reinforcement Learning and the Markov decision process, specifically the Markov Chain approach, in developing an improved model for prediction. The evaluation of this research involved brainstorming on the Bellman’s equation with the aid of the Markov Chain transition state framework, resulting in an improved mean value function of 71.7. This indicates an enhanced comprehension state for the learning students compared to the existing mean value function of 46.0. The results obtained from this research clearly demonstrate that the improved model was able to predict and assign the best learning path to achieve optimum comprehension state for learners. https://journal.iitta.gov.ua/index.php/itlt/article/view/5530Machine LearningMarkov ChainMarkov Transition State DiagramIntelligent Tutoring SystemComputer-based LearningMarkov Decision Process |
| spellingShingle | Ifeanyi Isaiah Achi Chukwuemeka Odi Agwu Christopher Chizoba Nnamene Sylvester C. Aniobi Ifebude Barnabas C. Kelechi Christian Oketa Godson Kenechukwu Ezeh John Otozi Ugah PREDICTING THE LEARNING PATH TO LEARNER’S OPTIMUM COMPREHENSION Інформаційні технології і засоби навчання Machine Learning Markov Chain Markov Transition State Diagram Intelligent Tutoring System Computer-based Learning Markov Decision Process |
| title | PREDICTING THE LEARNING PATH TO LEARNER’S OPTIMUM COMPREHENSION |
| title_full | PREDICTING THE LEARNING PATH TO LEARNER’S OPTIMUM COMPREHENSION |
| title_fullStr | PREDICTING THE LEARNING PATH TO LEARNER’S OPTIMUM COMPREHENSION |
| title_full_unstemmed | PREDICTING THE LEARNING PATH TO LEARNER’S OPTIMUM COMPREHENSION |
| title_short | PREDICTING THE LEARNING PATH TO LEARNER’S OPTIMUM COMPREHENSION |
| title_sort | predicting the learning path to learner s optimum comprehension |
| topic | Machine Learning Markov Chain Markov Transition State Diagram Intelligent Tutoring System Computer-based Learning Markov Decision Process |
| url | https://journal.iitta.gov.ua/index.php/itlt/article/view/5530 |
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