Learning analytics based on educational chatbot interaction and performance data | |
| Author | Nabila, Nashia Ahmed |
| Call Number | AIT Thesis no.IM-22-02 |
| Subject(s) | Chatbots Education--Data processing Educational technology Data mining |
| Note | A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Information Management |
| Publisher | Asian Institute of Technology |
| Abstract | Conversational bots in education have been growing extremely popular day by day. These programs are utilized as alternate to direct teacher for lesson and also able to track and store student actions throughout the learning process. Researchers are growing intereste on this data for learning analytics and creating student behavioral prediction models or student profiling. Thailand has used Scratch in basic education to boost computational thinking ability of the kids. But UNESCO has detected shortage of suitable instructor in many provincial institutions in compared to number of students, for which ScratchThAI chatbot was launched to aid students directly by sending learning materials, exercises, as well as other helps. The major purpose of my study is to allow instructors to enhance their intervention plan and teaching effectiveness by recognizing distinct group of students. During a session designed to introduce ScratchThAI to three distinct educational institutions, two types of data were collected for this study: chatbot interaction and performance data. Then, exhaustive experiments were undertaken to establish the most effective clustering approach in terms of performance evaluation and clustering quality. Five student groups were found using the K-means clustering technique, which yielded the best results in terms of classifying students according to their characteristics. Later, a model for early prediction was developed employing clusters as the model's classifier to identify various student groups in advance. Based on several performance matrices, the XGBoost classification model yielded the best outcome. For teachers to readily comprehend the features of different student groups and individuals, a visualization dashboard was constructed. Finally, I suggested implementing the selected prediction model into the dashboard so that teachers can identify student groups earlier in the semester. |
| Year | 2022 |
| Type | Thesis |
| School | School of Engineering and Technology |
| Department | Department of Information and Communications Technologies (DICT) |
| Academic Program/FoS | Information Management (IM) |
| Chairperson(s) | Chutiporn Anutariya |
| Examination Committee(s) | Dailey, Matthew N.;Vatcharaporn Esichaikul |
| Scholarship Donor(s) | His Majesty the King's Scholarships (Thailand) |
| Degree | Thesis (M. Eng.) - Asian Institute of Technology, 2022 |