Evaluating an Automated Analysis using Machine Learning and Natural Language Processing Approaches to Classify Computer Science Students' Reflective Writing

Alrashidi, H.; Almujally, N.; Kadhum, M.; Ullmann, T. and Joy, M. (2022). Evaluating an Automated Analysis using Machine Learning and Natural Language Processing Approaches to Classify Computer Science Students' Reflective Writing. In: Pervasive Computing and Social Networking. Lecture Notes in Networks and Systems, vol 475 (Ranganathan, G.; Bestak, R. and Fernando, X. eds.), pp. 463–477.

DOI: https://doi.org/10.1007/978-981-19-2840-6_36

Abstract

Reflection writing is a common practice in higher education. However, manual analysis of written reflections is time-consuming. This study presents an automated analysis of reflective writing to analyze reflective writing in CS education based on conceptual Reflective Writing Framework (RWF) and application of natural language processing and machine learning algorithm. This paper investigates two groups of features extraction (n-grams and PoS n-grams) and random forest (RF) algorithm that utilize such features to detect the presence or absence of the seven indicators (description of an experience, understandings, feelings, reasoning, perspective, new learning, and future action). The automated analysis of reflective writing is evaluated based on 74 CS student essays (1113 sentences) that are from the final year project reports in CS’s students. Results showed the seven indicators can be reliably distinguished by their features and these indicators can be used in an automated reflective writing analysis for determining the level of students’ reflective writing. Finally, we consider the implications of how the conceptualization of refection quality and providing individualized learning support to students in order to help them develop reflective skills.

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