Design and Implementation of a System for Predicting Student Performance Using Artificial Neural Network
CHAPTER ONE
OBJECTIVES OF THE STUDY
The following are the objectives of this study:
- To examine the use of Artificial Neural Network in predicting students academic performance.
- To examine the mode of operation of Artificial Neural Network.
- To identify other approaches of predicting students academic performance.
CHAPTER TWO
LITERATURE REVIEW
INTRODUCTION
This chapter gives an insight into various studies conducted by outstanding researchers, as well as explained terminologies with regards to predicting students academic performance using artificial neural network. The chapter also gives a resume of the history and present status of the problem delineated by a concise review of previous studies into closely related problems.
THEORETICAL FRAMEWORK
Intelligence and the g-factor are the most frequently studied factors in relation to academic achievement and the prediction of performance (Miñano et al., 2012). There is a large body of research that shows a strong positive correlation between g and educational success (e.g., Kuncel, Hezlett, & Ones, 2001; Linn & Hastings, 1984). The g-factor is defined, in part, as an ability to acquire new knowledge (e.g., Cattell, 1971; Schmidt, 2002; Snyderman & Rothman, 1987). Although the g-factor is not the same construct as Working Memory (WM), several studies have demonstrated a high correlation between these measures (Heitz et al., 2006; Unsworth, Heitz, Schrock, & Engle, 2005). Following the early study of Daneman and Carpenter (1980) on individual differences in working memory capacity (WMC) and reading comprehension, further research has shown the importance of WMC as a domain-general construct (Conway, Cowan, Bunting, Therriault, & Minkoff, 2002; Conway & Engle, 1996; Engle & Kane, 2004; Feldman Barrett, Tugade, & Engle, 2004; Kane et al., 2004), including the prediction of average scores over several academic areas (Colom et al., 2007).
Similarly, a large body of literature shows WMC as a very important construct in several areas and several studies have shown its importance in a wide range of complex cognitive behaviours such as comprehension (e.g., Daneman & Carpenter, 1980), reasoning (e.g., Kyllonen & Christal, 1990), problem solving (Welsh, Satterlee- Cartmell, & Stine, 1999) and complex learning (Kyllonen & Stephens, 1990; Kyndt, Cascallar, & Dochy, 2012; St Clair-Thompson & Gathercole, 2006). WMC is an important predictive variable of intellectual ability and academic performance, consistent over time (e.g. Engle, 2002; Musso & Cascallar, 2009a; Passolunghi & Pazzaglia, 2004; Pickering, 2006). Working memory is a paradigmatic form of cognitive control that explains how this cognitive control occurs, and which involves the active maintenance and executive processing of information available to the cognitive system, combining the ability to both maintain and effectively process information with minimal loss (Jarrold & Towse, 2006). It is crucial for the processing of information within the cognitive system, it has a limited capacity and it differs between individuals (Conway et al., 2005). The literature seems to indicate two fundamental approaches according to the interpretation of working memory and executive control. Traditional perspectives represent working memory and executive control as separate modules (e.g., Baddeley, 1986). The perspective taken in this research coincides with another view that understands working memory and executive control as constituting two sides of the same phenomenon, an emergent property from the neuro-cognitive architecture (Anderson, 1983, 1993, 2002, 2007; Anderson et al., 2004; Hazy; Frank & O‟Reilly, 2006).
CHAPTER THREE
RESEARCH METHODOLOGY
INTRODUCTION
This chapter covers the description and discussion on the various techniques and procedures used in the study to collect and analyze the data as it is deemed appropriate.
RESEARCH DESIGN
According to Asika (2009), research designs are often referred to as the structuring of investigation aimed at identifying variables and their relationships to one another. In this study, questionnaire serves as useful guide to the effort of generating data for this study. The survey research design through the administration of questionnaires was used for the study.
AREA OF THE STUDY
The study will be conducted in Lagos State, Nigeria. Lagos is the most populous city in Nigeria, the second fastest-growing city in Africa and the seventh in the world. The population of Lagos urban area, according to the Lagos State Government is 17.5 million, a number disputed by the Nigerian Government and judged unreliable by the National Population Commission of Nigeria.Lagos was reported in 2014 to have a metropolitan population of 21 million, making Lagos
CHAPTER FOUR
DATA ANALYSIS AND INTERPRETATION
PRESENTATION OF DATA
The final sample included 786 university students from several disciplines (Psychology, Engineering, Medicine, Law, Social Communication, Business and Marketing), in three private universities, during the 2009-2011 academic years.
Descriptive statistics of the cognitive variables and learning strategies are presented in Table 1 (cognitive variables) and Table 2 (learning strategies).
CHAPTER FIVE
SUMMARY OF FINDINGS AND CONCLUSION
SUMMARY OF FINDINGS
The purpose of this study was to show the applicability and the effectiveness of the ANN approach to the predictive classification of students in the full range of academic performance (GPA), as well as to identify and understand the importance of the variables for each level (low, middle and high) of expected GPA. This methodology, using a predictive system, was chosen as it is very effective under conditions of very complex and great amount of data, in which a large number of variables interact in various complex and not very well understood patterns.
The results attained in this study have allowed the identification of the specific influence of each input set of variables on different levels of academic performance (high and low performance), on one hand, and common processes across all students, on the other hand. One important contribution of this predictive approach is the finding that the same variables have different effects in each group of students, defining specific patterns for each performance level. Although the contribution of each variable in a particular pattern carries a relatively small predictive weight, it is the combined effect of the pattern of variables which explains a lower or higher academic performance model.
Among the student group with the lowest 33% of academic performance, two main predictors are learning strategies components (cognitive resources/cognitive processing and time management). The importance of learning strategies as a mediating factor in a model predicting academic performance has been shown in different studies (Dupeyrat & Marine, 2005; Fenollar, et al., 2007; Simons et al., 2004; Weinstein & Mayer, 1986; Weinstein et al., 1987; Weinstein et al., 1982). However, this study added the contribution of a complex pattern of variables for a particular group of students, identifying specific learning strategies that help the classification of students in a low performance group (i.e., thoughts or behaviours that help to use imagery, verbal elaboration, organization strategies, and reasoning skills). Included in this set are learning strategies that help build bridges between what they already know, and what they are trying to learn and remember (i.e., knowledge acquisition, retention, and future application). In addition, variables related to speed of processing involved in WMC functioning have an important predictive weight for the determination and modelling of the low performance group. Other studies that have used ANN have also found that basic cognitive processing variables such as WMC and Executive Attention carried the most predictive weight in the low performance group of students (Kyndt et al., 2012, submitted; Musso & Cascallar, 2009a; Musso et al., 2012). Moreover, the literature has indicated the positive association between WMC and academic achievement (Gathercole, Pickering, Knight, & Stegmann, 2004; Riding, Grimley, Dahraei, & Banner, 2003). Regarding the relative importance of each variable, if we compare the relative role of WMC and other cognitive resources between the low and high performance groups, WMC and cognitive resources were far more important for lower GPA students. The fact that their importance for the prediction is much greater for the lower performing group is greatly due to the fact that all members of the high group had higher levels of WMC and cognitive resources, therefore not providing the necessary information to the network. On the other hand, it was an identifying characteristic of the low performing group which had consistently lower values of WMC and cognitive resources. Remediation programmes, tutorial systems and instruction methods should consider these specific learning strategies, cognitive processing characteristics and WMC resources, in order to provide basic support to students at risk. Such informed interventions would improve the possibilities of successful academic achievement for the at-risk groups, including those with particular learning difficulties.
CONCLUSION
In conclusion, the current predictive systems approach facilitates and maximizes the identification of those factors (or predictors) of the learning processes which participate in varying degrees in the modelling of different levels of performance in academic outcomes in higher education. If we can identify specific profiles of students, focusing on the most important variables, this opens major possibilities for the improvement of assessment procedures and the planning of pre-emptive interventions. Given that this methodology allows for the accurate prediction of actual academic performance at least one academic year in advance to it actually being measured (GPA), it has implications for the application of these methods in educational research and in the implementation of-warning”diagnosticprogrammes“early settings. These results also inform cognitive theory and help in the development of improved automated tutoring and learning systems. Although some of the variables involved, such as educational level of the parents, are impossible to alter in their effects on academic performance at the time of the assessment, they do inform policy and indicate the weight that many social and environmental factors influence future academic performance. This methodological and conceptual approach allows us to consider a large number of variables simultaneously and select those which are most relevant and allow a greater degree of intervention to improve student performance, including early intervention programmes for students in need of special support.
The capacity to very accurately classify expected student performance, which is also what tests attempt to do, without the performance sampling issues of traditional testing, and using a much broader spectrum of all factors influencing a student‟s ov methodology. In fact, it also represents a more valid approach to educational assessment due to its overall accuracy and the breadth of the constructs considered to classify the expected performance. Traditional assessments are not sufficient for more complex assessments or for assessment systems that intend to serve multiple direct and indirect purposes, in complex educational situations (Mislevy, 2013; Mislevy, Steinberg, & Almond, 2003) In this respect, this new approach allows for the conceptualization and development of new modes of assessment which could facilitate breaking away from traditional forms of testing while at the same time improving the quality of the assessment process (Segers, Dochy & Cascallar, 2003).
Finally, the use of ANN together with other methods as cluster analyses and Kohonen networks could contribute to the study of the specific patterns of those variables which influence the learning process for each level of performance. In fact, a major observation resulting from the data in this study is that variables contribute to the prediction in relatively small proportions, and it is the joint effect of many contributing variables that could cause significant changes in performance. In other words, there is no “magic rather the accumulation of effects from all these various sources that produces significant changes in outcomes. These results provide an insight into learning questions from a different perspective and one that has important implications for educational policy and education at large.
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