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A mixed methods intervention to enhance the mathematics capability of first-year information technology students at a university of technology
Author(s)
Freitas, Jane Nelisa
Date Issued
2020
Type
Thesis
Publisher
Cape Peninsula 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.
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.
Additional information
Thesis (DTech (Information Technology))--Cape Peninsula University of Technology, 2020
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