1.3 Research Questions
The succeeding research questions will guide the research study.
1. Is there an important relationship between students’ prior mathematics performance and that demonstrated on Basic Mathematics module?
2. Is there a substantial link between students’ academic self-regulation and performance demonstrated on Basic Mathematics module?
3. Is there a noteworthy connection between students’ learning styles and performance demonstrated on Basic Mathematics module?

4. How does the low performance in Basic Mathematics affect students?
5. Is the teaching methods used at the University has any influence on student poor performance in the basic mathematics module?
6. What should be done to avoid the factors that contribute to poor performance in Basic Mathematics module?

1.4 Significance of the Study
The purpose of this study is to inspect the factors that contribute to poor performance of students who did Basic Mathematics Module. This study will aim to institute the strategies that can be adopted to improve performance among students who are specializing in Mathematics at the University of Namibia, HP Campus. The findings of this study will provide students and lecturers with new perceptions into developing issues on performance in Basic Mathematics. This study is very vital as it will assist lecturers and students to be aware of the factors that contribute to low performance in Basic Mathematics Module and improve upon them.
1.5 Limitations of the study
Due to the time limitation, this study will only focus on those students of HP Campus that have failed or repeating basic mathematics module.
1.6 Definition of the Terms
Prior mathematics performance: means performance demonstrated at High school level considered eligible for acceptance in the university/programme.
Factors: According to Vulda (2012), factors are Elements contributing to a particular result or situation. Insufficient time is one of the factors in poor performance.
Academic self-regulation factors: refer to students’ ability to monitor, evaluate and make plans for their learning their study habits.
Mathematics: Are the study of objects and their relations. Examples of objects are quantity, structure, space and change Vulda ( 2012).
Learning style: is the diverse ways that students learn.
Poor performance: A performance that is adjudged by the examinees or testees as falling below an expected or desired standard (Aremu, 2000).
Probability sampling: Is defined as the selection of individuals or elements to be the sample representative of the population which ensures that each individual in the population has an equal and fair chance of being part of the sample Abdullahi (2013).
The literature has a surplus of investigations designed to identify factors that apply some influence on students’ performance in academic settings. Among the factors that the researcher will identify as predictors will be prior mathematics achievement, poor performance, self-regulation, learning style, and solution.
2.1. Prior Mathematics achievement
Of all the concepts in the cognitive domain, the one considered the strongest forecaster of academic success is the prior academic achievement (ACT, 2007) which according to Sxhiefe and Csikszentmihalyi (1995) strongly effects students’ mathematical ability. Morrison and Schmit ( 2010), using logistic reversion analysis, found that American College Testing (ACT) mathematics score and high school GPA were significant predictors of achieving a C or better in Mathematics for Liberal Arts. Similarly, Hailikari, Nevgi, and Komulainen (2008) in a study involving 139 students at a university found that prior knowledge was the strongest predictor of student achievement on mathematics. According to ACT (2007), prior knowledge does not only affect student performance but also determines student persistence in college and serve to reduce the difference in success among racial/ethnic and income groups. But, research by Rech and Harrington (2000) into the effect of ACT on mathematics achievement between black and white men found a significant difference between their scores even though their mathematics background were similarly based on ACT mathematics score. Given the relationship of prior mathematics achievement to subsequent mathematics achievement, the significance of the variable is worth investigating in the light of the troubling mathematics performance demonstrated at Basic Mathematics module. Besides prior mathematics achievement, students’ self-efficacy beliefs have also been found to affect academic performance.
2.2. Self-regulation
Self-regulation may be defined as the ability of students to monitor, evaluate and make appropriate plans for their learning. According to Kitsantas(2002) and Zimmerman (2008) cited in Kitsantas, Winsler, and Huie (2008)academic self-regulation is displayed by students who are independent, self-initiated learners with the ability to use a variety of learning strategies, such as organizing, transforming, note taking, to accomplish specific learning goals.
In a study conducted by Benford and GessNewsome (2006) suggested that students who withdrew, failed or obtained poor grades in gateway courses that included mathematics, used ineffective study skills. A meta-analysis conducted by Robbins, et al. (2004) into the relationship between psychological and study skills factors and college outcomes suggested that study skills are precursors of positive class performance, which later drive achievement and persistence behavior. Additionally, incomplete support was found for the effect of other self-regulatory practices such as time management in predicting academic performance during the first year and influencing the second year performance as well (Kitsantas, Winsler, ; Huie, 2008).
Zimmerman (1990, p. 14) believed that self-regulated learners usually employ systematic metacognitive, motivational and behavioral tactics to learning, are responsive to feedback concerning their learning and hold strong views of academic accomplishments. He further recommended that instructional approaches which focus on the metacognitive, motivational and behavioral aspects of learning should be utilized and that attempts to stand-in these dimensions in students would stimulate long-lasting academic learning. Effective study skills have some influence on student performance and were demonstrated the student is likely to exhibit independence using a variety of learning styles to achieve and improve their academic performance in Basic Mathematics module.
2.3. Learning Styles
The term learning style refers to the concept that individuals differ in regard to what mode of instruction or study is most effective for them (Pashler, Daniel, Rohrer, and Bork, 2008, p. 105). According to Silver, Strong, and Perini (2000), the concept dates back to ancient Greek all the way to the Renaissance. They linked the learning style concept to Hippocrates “FOURNES” which when not in equilibrium cause persons to exhibit four types of personalities and William Blake’s description of the four Zoas of human existence: the body and its senses; the heart and its capacity for love; the head and its ability to reason; and the spirit and its potential for creative imagination seem similar to that of Hippocrates. Silver et al. (2000) believed that evidence of the learning style concept can also be found in the spiritual stories of Indians of the North American Plains. The four human personality traits are given as wisdom, clarity of perception, introspection, and understanding one’s emotions. Carl Jung (1923) cited in Silver et al. (2000) reclassified human “FOURNESS” and advanced that humans use perception and judgment as cognitive functions to process information.
According to Jung perception is used to process information either through the senses or intuition while judgment is demonstrated through the logic of thinking or subjectivity of feeling. Jung’s model of the way people process information seems to have motivated educational researchers to develop the many theories regarding the learning styles of students. In this regard, Silver et al. (2000, p. 28) identifies four types of learners: the mastery learner who operates under the sensing thinking realm and learns best from drill, demonstration, practice , hands on experience; the understanding learner who operates under the intuitive-thinking realm and learns best through lectures, reading logical discussion and debates, and projects of personal interest; the interpretative learner who operates under the sensing-feeling realm and learns best from group experiences and projects, loving attention, personal expression and personal encounters, role playing; and the self-expressive learner who operates under the intuitive-feeling realm and learns best from creative and artistic activities, open ended discussions of personal and social values, activities that enlighten and enhance myths, human achievement, dramas.
Small ( 2001) suggest that the content of College Algebra should focus on real-world problems, emphasize problem-solving in the modeling sense, and include elementary data analysis. He opined that teaching should be student-centered, make use of appropriate technology and aim to develop communication skills via small group activities and projects to infuse positive experiences and confidence among students.
While no specific learning style was implied by Small the suggested learning activities could be used to describe the three types of learners identified by Silver et al. (2000) i.e. mastery, intuitive and interpretative. Further, Ng, Pinto, and Williams (2011)investigated the effects of learning styles on course performance of approximately forty students on a business statistics course. They used an interpretive and learner-centered approach as well as learning activities that emphasized the applicability of the course studied to the real world. The study found that learning style was not a significant determinant of students’ overall course scores for the entire group of students despite designing the course to facilitate the diverse ways in which students processed information and emphasizing deeper approaches to learning. However, learning styles were significantly related to the average obtained at the examination for some subjects from the same sample used in the investigation discussed.
The findings of Ng et al. (2011) while specific to the survey statistics course investigated have implications for the performances demonstrated by Basic Mathematics Module students. The possible positive effect that accommodating the diverse ways in which students learn might have contributed to whatever increment in student achievement observed in the course. Limited, support is given by the “meshing hypothesis’ concept which posits that instruction is best provided in a manner that suits the learning style of students (Pashler et al., 2008). Herington and Weaven ( 2008) utilizing an action research approach to explore methods of improving the learning styles and outcomes of first-year university students within larges class environments found that the employment of student-centered teaching approaches did not enable students to employ deeper methods of self-regulating but served as a motivating tool. They attributed the lack of development of a more sustained deeper learning style to the previously developed learning style of students which may have to be unlearned before a new one is learned. They further opined that enabling students to transcend surface learning might pose a significant problem for tutors since acquiring a deeper learning style might entail several interventions.
Nevertheless, Riener and Willingham(2010) believe that the learning style theory is a myth. They agree that since differences among learners tend to affect their performance, they should be taken into account by teachers. They contend though those other factors such as learners’ ability, background knowledge, and interest vary from person to person and when learning styles are emphasized these important elements are neglected in the analysis of their effect on learning. On the contrary, Rech and Harrington (2000) believe that when mathematical backgrounds of ethnic groups are similar but mathematics achievement is different than the learning preference, among other variables, of this group should be investigated to determine what interventions could occur to improve performance. Clearly, any research into the factors that influence Basic Mathematics Module performance should also take cognizance of the preferred way in which students learn since the diverse findings indicate the possibility of the variable influencing performance at the course and examination level. Additionally, the findings may confirm or disconfirm the importance of utilizing a student-centered approach to the teaching of Basic Mathematics Module.