This research work is the result of multidisciplinary research for the design of a digital learning tool from the basic level. Computer science guarantees the technical concepts of the development of the tool. At the ...This research work is the result of multidisciplinary research for the design of a digital learning tool from the basic level. Computer science guarantees the technical concepts of the development of the tool. At the same time, Information and Communication Science discipline describes the concepts of cognitive psychology with digital competence in computerization. The overall goal of this work is to provide an HCI component for basic level learning or HCI for the illiterate. It is about transforming a communication tool from a cognitive system to a digital learning tool adapted to the learner’s level of digital competence. The author relies on the UML class diagram to identify the affordance and serendipity of HCI property entities. The mathematical study of the similarity between these two properties allows us to update the learner’s profile. Stephanie’s approach was used when managing the learner’s profile and the digital competency taxonomy to classify the learner’s level of digital use. The principle of an expert system was taken up in this work to make the decision on the level of digital competence of the learner and to guide the solution so that the learning continues without interruption. This expert system integrates the data science of digital educational materials, defining its specificity. The algorithm of the realization summarizes the implementation of the whole approach. αβGasy@mobile is an application on Android for Malagasy language literacy. To learn the letter “l” with this application, the initially low-level learner had an intermediate assessment score after 2 hours of continuous learning. Self-constructivism has been developed and strengthened by the HCI illiterate component. We have hundreds of learners for the case study and used ten smartphones to 5 inches. On the selected samples, the results of the experiment are close to our objective. More precisely, the inactive time is almost non-existent, the learning time predefined in the system is close to reality, and the speed of return on the interaction is fluid while the evaluation is notified.展开更多
文摘This research work is the result of multidisciplinary research for the design of a digital learning tool from the basic level. Computer science guarantees the technical concepts of the development of the tool. At the same time, Information and Communication Science discipline describes the concepts of cognitive psychology with digital competence in computerization. The overall goal of this work is to provide an HCI component for basic level learning or HCI for the illiterate. It is about transforming a communication tool from a cognitive system to a digital learning tool adapted to the learner’s level of digital competence. The author relies on the UML class diagram to identify the affordance and serendipity of HCI property entities. The mathematical study of the similarity between these two properties allows us to update the learner’s profile. Stephanie’s approach was used when managing the learner’s profile and the digital competency taxonomy to classify the learner’s level of digital use. The principle of an expert system was taken up in this work to make the decision on the level of digital competence of the learner and to guide the solution so that the learning continues without interruption. This expert system integrates the data science of digital educational materials, defining its specificity. The algorithm of the realization summarizes the implementation of the whole approach. αβGasy@mobile is an application on Android for Malagasy language literacy. To learn the letter “l” with this application, the initially low-level learner had an intermediate assessment score after 2 hours of continuous learning. Self-constructivism has been developed and strengthened by the HCI illiterate component. We have hundreds of learners for the case study and used ten smartphones to 5 inches. On the selected samples, the results of the experiment are close to our objective. More precisely, the inactive time is almost non-existent, the learning time predefined in the system is close to reality, and the speed of return on the interaction is fluid while the evaluation is notified.
