Abstract:

Non-STEM students frequently undervalue vital data skills such as reading, analysing, arguing with data, and numerical proficiency. Many in non-STEM courses perceive barriers, mistakenly thinking these skills are exclusive to STEM disciplines. To overcome this misconception, tailored teaching approaches are crucial for breaking down barriers, integrating non-STEM students into the learning process, and enhancing their data skills.

This study implemented an inquiry-based and collaborative-based learning in the Data Analysis, Tools, and Applications module in the academic year 2021-2022 which consisted of 140 students (23 computing, 117 business). The 50-minute online session used Mentimeter for engagement through questioning, quizzes, and polls, integrating real-world case scenarios. A 2-hour on-campus practical session implemented collaborative learning, wherein student groups collaboratively tackled problems or tasks related to the weekly topic. This involved brainstorming and collective problem-solving efforts. Evaluation comprised a self-directed questionnaire adapted from the National Research Council of the United States National Academies of Sciences, covering various aspects. The post-teaching formative test assessed different learning levels using multiple styles (MCQ, Drop-in answer, Fill-in-the-blank) based on Bloom’s Taxonomy—recalling information, summarising ideas, and applying knowledge to new problems.

Of the 117 business students, 35% (41 students) responded to the questionnaire. Notably, 59% found lectures satisfactory, 47% commended the inquiry-based approach, and 48% praised visual aids. Instruction and online materials were considered fine by 55%, and 62% rated assessments as fine. Overall, students expressed strong positive opinions. Regarding the learning experience, 55% found the pace suitable, and 55% felt the content difficulty was manageable. 52% of Students expressed they learned in a fair amount. In the formative assessment with 76 participants (63 business, 13 computing), the average score was 20.64 out of 35 (SD = 5.32). An independent-samples t-test comparing average formative test scores in STEM and non-STEM students showed no significant difference, t(74) = -1.66, p = .10, despite slightly higher scores for STEM students (M=22.85, SD=6.22) compared to non-STEM students (M=20.19, SD=5.06). The results suggest no significant difference in data skills between the two groups. Importantly, the findings indicate a reduced skill gap by the end of the sessions, demonstrating that non-STEM students can achieve knowledge and data skills similar to STEM students.

The study reveals that inquiry and collaborative learning positively impact students in non-STEM courses, enabling them to acquire data skills traditionally associated with those in STEM domains. Effective teaching of foundational data skills to non-STEM students is achievable with appropriate methods. The 'learning by doing' approach proves instrumental in breaking down disciplinary barriers, fostering a seamless transfer of knowledge and skills. These findings emphasise the need for further exploration and implementation of inquiry and collaborative-based methodologies in education across diverse settings, aiming to broaden the scope of student learning.

Keywords:

Non-STEM, Data Skills, Inquiry-based learning, Collaborative Learning.