Use of machine learning (text recognition, natural language processing, and large language models) for hand-written answer sheet evaluation
| dc.contributor.author | Mutugi, B. | |
| dc.date.accessioned | 2025-04-16T09:13:55Z | |
| dc.date.available | 2025-04-16T09:13:55Z | |
| dc.date.issued | 2024 | |
| dc.description | Full - text thesis | |
| dc.description.abstract | The realm of machine learning, encompassing text recognition, natural language processing and large language models, presents a transformative potential for the education sector, particularly in the evaluation of hand-written tests. This dissertation explored the use of these technologies in hand-written tests, acknowledging their prevalence and addressing inherent challenges encountered when evaluating the tests. The significant time required for evaluation often leads to delayed results and academic calendars, while the physical and mental strain on the evaluators, coupled with varying levels of skill and knowledge can lead to inconsistencies and inaccuracies in scoring. To address these challenges, this research explored the development of a machine learning approach capable of automatically extracting questions and student responses from images/pictures of exam papers and answer sheets. The approach then assessed student responses with the corresponding exam questions using pre-trained large language models. This research adopted the CRISP-DM (Cross-Industry Standard Process for Data Mining) framework —business understanding, data understanding, data preparation, modelling, evaluation, and deployment, to streamline the development and comparison of machine learning models. The result of was a machine learning model designed to process photos of question papers and answer sheets. It extracted text questions and answers, seamlessly facilitating the interaction between users and the technology. The textual content could then be analyzed by a pre-trained large language model, which performed the assessment and provided feedback. Enhancing the efficiency of assessments and elevating the accuracy and objectivity of feedback provided to learners, this approach promised to significantly reduce the time and effort involved in the evaluation process; thereby overcoming the limitations of current practices in hand-written test evaluation. | |
| dc.identifier.uri | http://hdl.handle.net/11071/15682 | |
| dc.language.iso | en | |
| dc.publisher | Strathmore University | |
| dc.title | Use of machine learning (text recognition, natural language processing, and large language models) for hand-written answer sheet evaluation | |
| dc.type | Thesis |