Abstract
Cognitive assistants help humans and enhance their capability to solve a large range of complex tasks. A cognitive assistant has developed a pedagogical assistant. This work aims to improve learning capabilities and helps to identify learning preferences. A cognitive assistant can hold conversations with users in natural language to help the user to solve a complex problem. The proposed system has been implemented to assist as a personal agent for students to learn Python programming language. The steps are user capability level identification, construction of assertion graph, QA analyzer, question analyzer, and primary search analysis, hypothesis generation, evidence identification and evidence scorers, final evidence identification user answer validation, and resource generation. The cognitive assistant facilitates natural interactions with the students, and it applies human reasoning skills to judge the student’s ability and train them further. Normal conversational is employed in question-answering systems. This increases users’ satisfaction and easily engages them with the system. A social dialogue and question-answering system has achieved significantly higher learning gains than a non-interactive online course. The result of the system is evaluated using the confidence-weighted score and expert judgments.
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Lakshmi, R.D., Murugappan, A., Srivani, M. (2023). Development of a Cognitive Question Answering System to Learn Concepts for Placement Assistance. In: Saini, H.S., Sayal, R., Govardhan, A., Buyya, R. (eds) Innovations in Computer Science and Engineering. ICICSE 2022. Lecture Notes in Networks and Systems, vol 565. Springer, Singapore. https://doi.org/10.1007/978-981-19-7455-7_29
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DOI: https://doi.org/10.1007/978-981-19-7455-7_29
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