Attitudes and achievement in statistics: a meta-analytic and functional near-infrared spectroscopy approach
- Authors: Wagenaar, Emma Kate
- Date: 2024-04-04
- Subjects: Uncatalogued
- Language: English
- Type: Academic theses , Master's theses , text
- Identifier: http://hdl.handle.net/10962/435490 , vital:73162
- Description: Statistics anxiety describes the extensive worry and apprehension that students may experience when faced with statistics content as part of their university curriculums. Student’s perfunctory disposition towards statistics has been indicated to negatively affect performance outcomes in statistics courses. Two meta-analyses were conducted to investigate the relationship between statistics anxiety and achievement in statistics. The first meta-analysis was inclusive of 22 studies investigating the relationship attitudes towards statistics and achievement, whilst the second meta-analysis focused on the relationship, primary amongst Psychology students. Student’s attitudes towards statistics were measured using the Survey of Attitudes Towards Statistics (SATS), whilst achievement in statistics courses was quantified using different outcome measures. Finding from the meta-analysis were supplemented by cortical mapping of the neural correlates of statistical reasoning using functional near-infrared spectroscopy (fNIRS). Results from the meta-analysis indicated a small significant relationship between university students’ Affect, r = 0.28, Value, r = 0.22 and Difficulty, r = 0.18, and subsequent achievement in statistics courses. A medium significant relationship between Cognitive Competence, r = 0.31, and achievement was also noted. Findings from the second meta-analysis, indicated a medium, significant relationship between Affect, r = 0.32, and Cognitive Competence, r = 0.35, and achievement. Moreover, a small significant relationship was found between Value, r = 0.24, and Difficulty, r = 0.23, in relation to achievement in statistics courses. Case study analysis of the neural correlates of statistics reasoning revealed varied signal quality findings of cortical mapping of the neural correlates of statistics in the dorsolateral prefrontal cortex (DLPFC). Moreover, seed-based correlation analysis indicated cortical activation of the dorsolateral prefrontal cortex paired with diverse prefrontal regions. Recommendations from the study include improvements to the fNIRS research design and the inclusion of larger samples to investigate the cortical mapping of the DLPFC in relation to statistics reasoning and statistics anxiety. , Thesis (MA) -- Faculty of Humanities, Psychology, 2024
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Performance evaluation of baseline-dependent window functions with several weighing functions
- Authors: Vanqa, Kamvulethu
- Date: 2024-04-04
- Subjects: Uncatalogued
- Language: English
- Type: Academic theses , Master's theses , text
- Identifier: http://hdl.handle.net/10962/435850 , vital:73206
- Description: Radio interferometric data volume is exponentially increasing with the potential to cause slow processing and data storage issues for radio observations recorded at high time and frequency resolutions. This necessitates that a sort of data compression is imposed. The conventional method to compress the data is averaging across time and frequency. However, this results in amplitude loss and source distortion at the edges of the field of view. To reduce amplitude loss and source distortion, baseline-dependent window functions (BDWFs) are proposed in theliterature. BDWFs are visibility data compression methods using window functions to retainthe signals within a field of interest (FoI) and to suppress signals outside this FoI. However,BDWFs are used with window functions as discussed in the signal processing field without any optimisation. This thesis evaluates the performance of BDWFs and then proposes to use machine learning with gradient descent to optimize the window functions employed in BDWFs. Results show that the convergence of the objective function is limited due to the band-limited nature of the window functions in the Fourier space. BDWFs performance is also investigated and discussed using several weighting schemes. Results show that there exists an optimal parameter tuning (not necessarily unique) that suggests an optimal combination of BDWFs and density sampling. With this, ∼ 4 % smearing is observed within the FoI, and ∼ 80 % source suppression is achieved outside the FoI using the MeerKAT telescope at 1.4 GHz, sampled at 1 s and 184.3 kHz then averaged with BDWFs to achieve a compression factor of 4 in time and 3 in frequency. , Thesis (MA) -- Faculty of Science, Mathematics, 2024
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