A mixed methods intervention to enhance the mathematics capability of first-year information technology students at a university of technology

Abstract

This study investigated the efficacy of a Mathematics Capability Intervention (MCI) module within Quantitative Techniques (QT) course, part of a Higher Certificate in Information and Communication Technology (HCINCT) programme. Poor mathematical capabilities contributed to the problem of unsatisfactory throughput rates in IT programming courses. The level of High School mathematics compounded the situation. Analysis of student mathematics knowledge, highlighted that performance of many of the students was lower than that of 11 to 12-year-old pupils. An MCI module was designed and scheduled within Quantitative Techniques (QT) tutorial classes among one hundred and forty-seven first year ICT students. The intervention was administered in 2015 to 147 information technology freshmen through class tutorials in the subject of Quantitative Techniques. Six research questions drove the investigation. The mixed methods sequential explanatory design approach was used to collect analyze and present the data. The main research question of the study was: What is the effect of the intervention programme on the mathematical knowledge of IT students upon entry into the HCINCT programme? To find answers to the main research question, hypotheses were constructed based on historical data. A mixed methods sequential explanatory design (Creswell, 2013) underpinned the study. Quantitative instruments collected data derived from three formative pre-test assessments. Post-test assessments were conducted for two reasons: • To ascertain whether students had acquired the desired skills; and • To measure potential change in mathematics knowledge. The qualitative aspect of the study comprised a purposively selected sample of eleven students agreed to participate in semi-structured interviews, contributing qualitative data in response to open-ended questions. The null hypothesis (Ho) claimed that there was no statistically significant difference between the mean scores of the groups before and after the MCI (the treatment). The Solomon 1949 guidelines were used by the researcher to assign the students randomly into groups. The analysis was performed using four groups: Experimental Group 1 (E1), Control Group 1 (C1), Experimental Group 2 (E2) and Control Group 2 (C2). Students’ pretest and post-test scores were evaluated to find answers to the main research question. The critical values used were t-test values gained from IBM SPSS (version 25) data outputs. Sub-question 1 enquired: Are the post-test scores of all groups significantly statistically different? The null hypothesis claimed that the means of each group were the same. To support the null hypothesis of sub-question 1, the students’ post-test scores were evaluated by performing a one-way (between groups) ANOVA. Sub-question 2 enquired: What evidence do we have to suggest that the sample came from a population for which the mean score was 50? The null hypothesis claimed that there was no statistically significant difference between the mean score of the students at the University of Technology where the study was conducted and the mean score of the HCINCT 2015 students. To test the null hypothesis, the final-year assessment scores of the HCINCT students were used. Sub-question 3 enquired: Is there a statistically significant difference between the HCINCT 2016 mean score and the HCINCT 2015 mean score on exit? To support the null hypothesis, the final-year assessments of the HCINCT students were evaluated. Sub-question 4 enquired: Under which circumstances did the students’ results improve? The null hypothesis claimed that there were no improvements in the students’ results. Teaching methods and the students’ interview responses were evaluated for answers. It emerged that teaching methods provided the best answer. The descriptive statistics results showed that there was a noticeable difference between the mean values for E1 scores. The mean (X̅ ) for the C1 pre-test (X̅ = 39.62, SD = 6.3) was not noticeably different from the C1 post-test (X̅ = 39.16, SD = 5.0). The researcher further assessed the differences between these two sample means using a paired samples t-test to assess statistically significant differences between the scores on the pre-test and the post-test measures for groups E1 and C1. A further analysis was performed to determine whether or not the means of the 4 post-test groups were significantly different from one another. A one-way between groups ANOVA was performed. There was a significant effect between the mathematical knowledge scores of the 4 post-test scores (E1, C1, E2 and C2. Post hoc comparisons using the Tukey HSD test indicated that the mean score for the E1 group was significantly different than the C1 group. However, the E1 group did not significantly differ from the E2 group. The two control groups, C1 and C2 did not differ from one another. Since there was a question of normality for the C1- Pre Test group as determined by the Shapiro (p< .05), a Kruskal-Wallis test was conducted comparing the results of the Post-Test for all 4 groups: E1, E2, C1, C2. Statistical analysis of quantitative outcomes indicates post-test scores of the IT students in an experimental group who experienced the intervention treatment demonstrated enhanced mathematical knowledge. No such improvement was noted in a control group, not exposed to the MCI treatment. There is thus a probability that students’ mathematical capabilities improved, but not by chance. Quantitative findings are supported by qualitative data. In summary, the Mathematics Capability Intervention (MCI) module has the potential to influence mathematical knowledge of IT students in several ways by: • Confirming that students require a level of mathematical knowledge upon which to scaffold their quantitative skills; • Demonstrating that students should be encouraged to assume responsibility for acquiring mathematics knowledge themselves; • Supporting the synthesis of critical evaluation skills regarding quantitative techniques associated with in-class tutorial content; and • Highlighting the power of students’ desire to acquire mathematics capability. The efficacy of innovative mathematics interventions implemented among students within different universities is worthy of additional exploration. Further development of the MCI module could offer the UoT widespread and substantive benefits within different departments. The module could hereby cater for all mathematically at-risk students. Moreover, theoretical outcomes of the module design could inform adjustments to outdated syllabi, allowing for the inclusion of new and contextualised learning materials. QT class tutorials could focus more on allowing students to use quantitative techniques to solve real-world problems. Thus, students could be afforded constructivist opportunities to solve mathematical challenges in their own ways.