Predicting and preventing student failure - using the k-nearest neighbour method to predict student performance in an online course environment

Peer reviewe

Penerapan Data Mining Untuk Memprediksi Penerimaan Calon Mahasiswa Baru Fakultas Kedokteran Menggunakan Algoritma K-NN

Pada masa penerimaan mahasiswa baru, Fakultas Kedokteran masih menjadi tujuan utama lulusan sekolah menengah atas untuk melanjutkan pendidikannya. Untuk menjadi seorang mahasiswa kedokteran, calon mahasiswa diharuskan melakukan serangkaian tahapan. Selain itu, beberapa hal lain tentunya dapat menjadi poin penilaian dapat diterimanya calon mahasiswa. Penelitian ini berfokus pada prediksi penerimaan calon mahasiswa baru fakultas kedokteran ditinjau dari faktor-faktor yang mempengaruhinya. Untuk dapat memprediksi calon mahasiswa, dilakukan penerapan data mining dengan menggunakan algoritma K-NN. Dari percobaan yang dilakukan, diperoleh RMSE terbaik dengan data training 70 data yaitu sebesar 0.218 +/- 0.000. Untuk akurasi terbaik sebesar 76,1%. Dari penelitian ini dapat disimpulkan k-NN dapat digunakan untuk memprediksi penerimaan calon mahasiswa kedokteran, meski hasilnya belum maksimal

A Multi-Gene Genetic Programming Application for Predicting Students Failure at School

Several efforts to predict student failure rate (SFR) at school accurately still remains a core problem area faced by many in the educational sector. The procedure for forecasting SFR are rigid and most often times require data scaling or conversion into binary form such as is the case of the logistic model which may lead to lose of information and effect size attenuation. Also, the high number of factors, incomplete and unbalanced dataset, and black boxing issues as in Artificial Neural Networks and Fuzzy logic systems exposes the need for more efficient tools. Currently the application of Genetic Programming (GP) holds great promises and has produced tremendous positive results in different sectors. In this regard, this study developed GPSFARPS, a software application to provide a robust solution to the prediction of SFR using an evolutionary algorithm known as multi-gene genetic programming. The approach is validated by feeding a testing data set to the evolved GP models. Result obtained from GPSFARPS simulations show its unique ability to evolve a suitable failure rate expression with a fast convergence at 30 generations from a maximum specified generation of 500. The multi-gene system was also able to minimize the evolved model expression and accurately predict student failure rate using a subset of the original expressionComment: 14 pages, 9 figures, Journal paper. arXiv admin note: text overlap with arXiv:1403.0623 by other author

Smart Learning Environment: Paradigm Shift for Online Learning

Online learning has always been influenced by advanced technology. The role of online learning is expected not only for delivering contents to massive learners anywhere and anytime but also for promoting successful learning for the learners. Consequently, this emerged role has introduced the concept of smart learning environment. More specifically, smart learning environment is developed to promote personalized learning for learners. Personalized learning focuses on individual learner and provides appropriate feedback individually. Currently, the advances of modern technologies and intelligence data analytics have brought the idea of smart learning environment into realization. Machine learning techniques are generally applied to analyze real-time dynamic learner behavior and provide the appropriate response to the right learner. In this chapter, the evolution of online learning environment from different points of technological overviews is first introduced. Next, the concepts of personalized learning and smart learning environment are explained. Then, the essential components of smart learning environment are presented including learner classification and intervention feedback. Learner classification is to understand different learners. Intervention feedback is to provide an individual response appropriately. Additionally, some machine learning techniques widely used in smart learning environment in order to perform smart classification and response are briefly explained

A Formalism For PLAN – A Big Data Personal Learning Assistant For University Students

Big Data-based methods of learning analytics are increasingly relied on by institutions of higher learning in order to increase student retention by identifying at risk students who are in need of an intervention to allow them to continue on in their educational endeavors. It is well known that e-Learning students are even more at risk of failing out of university than are traditional students, so Big Data learning analytics are even more appropriate in this context. In this paper, we present our approach to this problem. We wish to place control of a student’s learning process in his own hands, rather than that of the learning institution in order to decouple the student from the institution since the goals and motivations of these two may not be completely aligned. In this way, we empower the student by giving him control of the personal learning system which employs Big Data techniques to generate recommendations on how to reach a set of learner-specific learning goals. We present the formalism which underlies our system, the architecture which implements the system, scenarios for system use, a survey of related works and thoughts on how the system will be implemented in a prototype in the future

Multi Agent Systems

Research on multi-agent systems is enlarging our future technical capabilities as humans and as an intelligent society. During recent years many effective applications have been implemented and are part of our daily life. These applications have agent-based models and methods as an important ingredient. Markets, finance world, robotics, medical technology, social negotiation, video games, big-data science, etc. are some of the branches where the knowledge gained through multi-agent simulations is necessary and where new software engineering tools are continuously created and tested in order to reach an effective technology transfer to impact our lives. This book brings together researchers working in several fields that cover the techniques, the challenges and the applications of multi-agent systems in a wide variety of aspects related to learning algorithms for different devices such as vehicles, robots and drones, computational optimization to reach a more efficient energy distribution in power grids and the use of social networks and decision strategies applied to the smart learning and education environments in emergent countries. We hope that this book can be useful and become a guide or reference to an audience interested in the developments and applications of multi-agent systems

Integrating Data Mining and Social Network Techniques into the Development of a Web-based Adaptive Play-based Assessment tool for School Readiness.

A major challenge that faces most families is effectively anticipating how ready to start school a given child is. Traditional tests are not very effective as they depend on the skills of the expert conducting the test. It is argued that automated tools are more attractive especially when they are extended with games capabilities that would be the most attractive for the children to be seriously involved in the test. The first part of this thesis reviews the school readiness approaches applied in various countries. This motivated the development of the sophisticated system described in the thesis. Extensive research was conducted to enrich the system with features that consider machine learning and social network aspects. A modified genetic algorithm was integrated into a web-based stealth assessment tool for school readiness. The research goal is to create a web-based stealth assessment tool that can learn the user's skills and adjust the assessment tests accordingly. The user plays various sessions from various games, while the Genetic Algorithm (GA) selects the upcoming session or group of sessions to be presented to the user according to his/her skills and status. The modified GA and the learning procedure were described. A penalizing system and a fitness heuristic for best choice selection were integrated into the GA. Two methods for learning were presented, namely a memory system and a no-memory system. Several methods were presented for the improvement of the speed of learning. In addition, learning mechanisms were introduced in the social network aspect to address further usage of stealth assessment automation. The effect of the relatives and friends on the readiness of the child was studied by investigating the social communities to which the child belongs and how the trend in these communities will reflect on to the child under investigation. The plan is to develop this framework further by incorporating more information related to social network construction and analysis. Also, it is planned to turn the framework into a self adaptive one by utilizing the feedback from the usage patterns to learn and adjust the evaluation process accordingly