Data Mining and E-Learning - Research Proposal Example

Data Mining and E-Learning Table of Contents Project 3 Aims and Objectives 3 Relevance to Professional or Academic Field 4 Research Approach orMethodology 7 Expected Outcomes 8 Proposed Time-Table of Activities 9 References 11 Project Title “Do data mining techniques contribute in knowledge enhancement through e-learning or it actually raises complexities, costs and time management challenges in the process?” Aims and Objectives E-learning has emerged as a new paradigm of knowledge management in the current era of modernisation, internationalisation and rapid transformations. The modern organisations and the educational institutions have therefore been investing substantially on this knowledge management through e-learning in order to reap benefits from global competitiveness and resourceful assistances availed in cross-border exchanges. In this regard, data mining is regarded as an important aspect in shaping the learning as well as teaching modes applied in a particular setting, signifying its role in intermediating the two most vital components of knowledge management (Batware, 2007). Data mining is often regarded as an inseparable facet of effective e-learning process aimed at better knowledge management. However, critics have also been of the view that data mining presents certain challenges that undermines its effectiveness as an ideal tool of knowledge management through e-learning (Ari, 2008; Monk, 2005). As argued in Abdullah (2008) and Chen & et. al. (2004), experiences of the data users in data mining and their accurate interpretation skills to use those data in knowledge management techniques when focusing on e-learning, often generate issues related with complexities and cost as well as time constraints. Emphasising this particular debated issue, the objectives of this particular research have been determined as the following. To determine the linkage between data mining techniques, e-learning approach and attributes of effective knowledge management To identify and analyse the contributory factors of e-learning in enhancing knowledge through effective data mining practices To critically assess the limitations of data mining that inhibit knowledge management efficiency in e-learning approach To develop a theory emphasising how data mining practices enhance knowledge management quality through e-learning mitigating its complexities (if any) in the procedure Relevance to Professional or Academic Field According to Moore & et. al. (2011), e-learning approach to knowledge management is fundamentally described as a web-based mechanism, which assists the users as well as the learners towards accessing knowledge or information in a convenient and time-efficient manner. In this similar concern, Moore & et. al. (2011) identified that the notion of e-learning emerged as one of the effective ideas in enhancing the knowledge of people through the incorporation of various innovative technological advancements. In this present day context, it can be apparently observed that data mining methods have been widely adopted in the application of e-learning for the purpose of solving numerous problems that arise while retrieving or gathering any data in today’s globalising world. As per the study conducted by Han & et. al. (2011), data mining is utilised in the field of statistics to support decisions in various fields of research including business or education, which tends to apply several techniques, neural networks, visualisation modes and decision trees among others. The various sorts of data mining techniques can be apparently observed as ‘link analysis’, ‘genetic algorithms’, ‘decision trees’, ‘artificial neutral networks’ and ‘automatic cluster detection’ among others. These techniques eventually help in performing various tasks effectively that entail classification, prediction as well as estimation, clustering, description along with profiling and affinity grouping and hence, contribute in the critical understanding of the issue in concern (Batware, 2007). The proposed research study would fit into the existing body of academic knowledge and practice in the professional world in terms of determining the contribution of several data mining methods in knowledge enhancement through e-learning approach. In this similar concern, Castro & et. al. (n.d.) noted that the initial step of any technique relating to data mining is to interpret the business or learning problems in a systematic process. Thus, in order to change a specific business model into a problem of data mining, the dependent variables are required to be appropriately defined, which at times often raises issues related to complexities subjected to the users’ ability (Batware, 2007). Previously conducted researches revealed a certain degree of dubiousness concerning the broader utilisation of e-learning approach with the application of data mining methods, asserting many limitations in the overall deliverance quality of the mechanisms in the field of knowledge enhancement (Monk, 2005). According to Hanna (2004), there are various functionalities of data mining techniques in the context of e-learning domain. As per the study made by Pattanaik & Ghosh (2010), the basic functionalities of data mining methods in e-learning include managing the respective activities of learning providers along with the users, keeping records of their information for future verification and referencing and maintaining necessary databases formed during the procedure. Pattanaik & Ghosh (2010) noted the basic functionality of data mining methods in the e-learning area is to assist the learners in providing suitable suggestions to them concerning any sort of complimentary or relevant course, which would be vital for them to effectively perform e-learning activities. In this similar concern, Hammouda & Kamel (n.d.) recognised the data mining methods to play major roles in various significant aspects with the incorporation of e-learning approach. These aspects comprise searching along with organising necessary documents, comparing these documents with others, extracting relevant information and finally summarising those documents in an effective manner. On the other hand, apart from playing the aforesaid roles in a favourable manner, Hammouda & Kamel (n.d.) identified certain loopholes or drawbacks in performing knowledge management with the application of data mining methods in the e-learning process. According to Hammouda & Kamel (n.d.), these drawbacks further raise certain complexities in the procedure that can be determined in terms of excessive cost burden and time limitation. Accordingly, Chen & et. al. (2004) affirmed that there exists a relationship between data mining and knowledge management through the utilisation of e-learning approach. This can be justified with the reference to the fact that data mining often enhances the knowledge of the investigators in exploring huge databases effectively and quickly despite having deficiency in training for performing the same. However, at the same time, Han & et. al. (2011) & Batware (2007) recognised certain instances wherein the involvement of e-learning approach in data mining techniques raised several complexities. These complexities can be measured in terms of increased level of costs and extreme reliance upon advanced technological advancements. It was further argued that although intertwined at various aspects, the concepts of e-learning, data mining and knowledge management are multidimensional and self-explanatory, which further raises significant concerns regarding the efficiency of aligning these variables to obtain the desired benefits (Han & et. al., 2011; Batware, 2007). As can be observed from the above conducted reviews of the literature, significant differences are observable as persisting amid the critics and users of data mining methods concerning its role and importance in the e-learning process. However, limited number of evidences has been obtainable when relating the implications of such limitations on knowledge management through e-learning process. Therefore, emphasising this particular literature gap, the proposed study is expected to determine the applicability of data mining techniques in the overall betterment of knowledge management through the e-learning approach. Research Approach or Methodology The proposed study will be based on a qualitative method, where focus will be delivered on developing a theoretical base in order to link the various attributes of data mining with the facets of e-learning and knowledge management, intended towards the identification of challenges generated and benefits rewarded by such a mechanism. A major reason to apply qualitative method in the proposed study is that the objectives determined for this study, steers the focus towards assessing the existing literature gaps in the identification of the concerning issue and developing a theoretical model thereupon, which might lack systematic interpretation and generated biases if qualitative methods are applied (Neergaard & Ulhoi, 2007). Accordingly, second-hand data, i.e. secondary data available through internet searches, literature sources and peer-reviewed articles will be considered in this proposed study to substantiate the determined study objectives. Notably, as the concerned issue in this study is theoretical and because the concept has been in limited use in the practical field of operations, obtaining reliable first-hand data through primary sources shall be challenging and shall quite likely generate hypothesised explanations. In addition, such information might result in delivering inadequate information to the persisting issue and hence, deliver a partial understanding of the intended concepts. Application of both secondary and primary sources, could have however mitigated the challenge of obtaining inadequate or partial understanding of the research issue, it shall also increase the risks of unsystematic interpretation and analytical errors due to complexities. Hence, only secondary sources have been considered as applicable in this proposed study. Furthermore, to analyse the gathered secondary data in a systematic and interpretive manner, a grounded theory approach will be implemented in the proposed study. Grounded theory is asserted as the most suitable qualitative analytical method when the aim of the study is focused on developing or discovering a new theory in practice (Calman, n.d.). As the grounded theory approach also exhibits a systematic flow of data analysis process, it is also expected that the theory developed in the proposed study would be reliable generating noteworthy future research scope. Accordingly, a theoretical sampling technique will be used in this process to accomplish the determined objectives for the proposed study. Expected Outcomes It is expected that reliable outcomes will be develop from this proposed study, which shall further prove effective in offering a new paradigm to the application of data mining techniques in the e-learning process and further contribute in better knowledge management. In this similar concern, one of the outcomes that might be resulted from the proposed project is determining the applicability of data mining in the development of knowledge management through e-learning approach in a wide ranging scenarios of business investigations, academic learning processes and other professional research areas. Moreover, the proposed research study shall deliver rational and more succinct apprehensions when applying the most suitable techniques of data mining to overcome potential challenges of complexities as well as time and cost constraints, which might result into the betterment of the currently practiced knowledge management procedure in various learning fields. It is worth mentioning that the proposed research project would deliver the results about why professionals in educational institutions and business or corporate sectors are much inclined towards making substantial investments on the application of the data mining process in their relative fields of operations as a tool for e-learning and further to enrich their knowledge process. As observed in the above explanation, such techniques are likely to raise challenges in terms of complexities and cost as well as time constraints, which are again expected to be mitigated substantially through the theoretical framework intended to be developed with the proposed study. Proposed Time-Table of Activities The proposed timetable of activities relating to the research study has been depicted hereunder. References Batware, J. B., 2007. Abstract. Real-Time Data Mining For E-Learning. [Online] Available at: http://libserv5.tut.ac.za:7780/pls/eres/wpg_docload.download_file?p_filename=F503457532/BatwareJB.pdf [Accessed March 10, 2014]. Castro, F. & et. al., No Date. Introduction. Applying Data Mining Techniques to e-Learning Problems. [Online] Available at: http://sci2s.ugr.es/keel/pdf/specific/capitulo/ApplyingDataMiningTechniques.pdf [Accessed March 10, 2014]. Chen, H. & et. al., 2004. Crime Data Mining: A General Framework and Some Examples. Research Feature, pp. 50-56. Calman, L., No Date. What is Grounded Theory? Manchester 1824. [Online] Available at: http://www.methods.manchester.ac.uk/events/whatis/gt.pdf [Accessed March 11, 2014]. Han, J. & et. al., 2011. Data Mining: Concepts and Techniques. Elsevier. Hanna, M., 2004. Data Mining in the E-Learning Domain. Emerald Group Publishing Limited. Moore, J. L. & et. al., 2011. E-Learning, Online Learning, And Distance Learning Environments: Are They The Same? Internet and Higher Education, Vol. 14, pp. 129-135. Monk, D., 2005. Using Data Mining for e-Learning Decision Making. The Electronic Journal of e-Learning, Vol. 3, Iss. 1, pp. 41-54. Neergaard, H. & Ulhoi, J. P., 2007. Handbook of Qualitative Research Methods in Entrepreneurship. Edward Elgar Publishing. Pattanaik, S. & Ghosh, P. P., 2010. Role of Data Mining in E-Payment Systems. International Journal of Computer Science and Information Security, Vol. 7, No. 2, pp. 262-266. Read More