Natural Language Processing and Machine Learning Approach to Teaching and Learning Research Philosophies and Paradigms

Abstract

The paper cogitates on the critical advent of 4th IR focusing on the concept of machine learning (ML) underpinned by natural language processing (NLP) to demonstrate how research philosophies and paradigms can be better taught and learned for students' benefit. A systematic literature review was earmarked for its depth and textual inquiry from scholarly arguments. The main purpose of this paper is to aver a progressive technological approach towards better comprehensive research paradigms and philosophies, which are complex domains with diverse variety in higher education and a cause of discomfort for students at post-graduate levels. Using quantitative algorithm, the natural language processing and machine learning-inspired digital model poses questions that place students in a reflexive mode and draws their articulated responses as inputs that model their worldviews against a host of philosophies in the database. The paper revealed that, discordant with previous scholars who advocated for a single philosophical assumption for a field, subject or researcher, such as the existence of a pure positivist and/ or pure interpretivist, purist philosophical assumptions should be challenged to benefit students and academics. It means that the digital discovery of research philosophies and paradigms extends the work of previous theorists to the technologically inspired discovery of episteme, ontology and axiology. By its nature, the use of NLP becomes an advanced channel on how we know what we know and the nature of the reality and values being displayed. The paper contributes to the evocation of deep learning arising from new philosophies and methods. The inquiry-based teaching approach transforms learning from the generic push-teaching method that assumes universality to the fostering of a reflexive approach that helps resolve the deep ideological approaches that caused the polarisation. The manner in which NLP and ML are able to extract information relevant to knowledge or philosophical discovery paves the way for approaches that can lead to the depolarisation and decolonisation of research philosophies, which can ultimately boost the development of research students.