Data analytics and programming for linguistics students: A SWOT and survey study

Abstract

Data analytics and programming skills are increasingly important in the humanities, especially in disciplines like linguistics due to the rapid growth of natural language processing (NLP) technologies. However, attitudes and perceptions of students as novice learners, and the attendant pedagogical implications, remain underexplored. This paper reports a combined SWOT (strengths, weaknesses, opportunities, threats) and survey analysis of how postgraduate linguistics students reflect on internal qualities and external circumstances that affect their learning. SWOT is a popular self-reflective strategic planning tool by organizations. An innovative approach was used to classify students into four SWOT-defined learner dispositions (SO, ST, WO, and WT) based on their relative emphasis on strengths vs. weaknesses, and opportunities vs. threats. Scores on a modified Mathematics Attitude Survey measuring self-rated ABILITY, INTEREST, UTILITY, and PERSONAL GROWTH were then compared across these dispositions. Results reveal i) some unexpected and interesting strengths/weaknesses/opportunities/threats, ii) perceived internal traits (strengths/weaknesses) play a greater role than external traits (opportunities/threats) in shaping students’ attitudes, iii) a paradox where more confident students tend to be less interested, and vice-versa. Pedagogical implications arising from the results are discussed with an eye on enhancing the teaching of data analytics and programming skills to this target population.