On the Use of Bayesian Probabilistic Matrix Factorization for Predicting Student Performance in Online Learning Environments

Thanks to the advances in digital educational technology, online learning (or e-learning) environments such as Massive Open Online Course (MOOC) have been rapidly growing. In the online educational systems, however, there are two inherent challenges in predicting performance of students and providing personalized supports to them: sparse data and cold-start problem. To overcome such challenges, this article aims to employ a pertinent machine learning algorithm, the Bayesian Probabilistic Matrix Factorization (BPMF) that can enhance the prediction by incorporating background information on the side of students and/or items. An experimental study with two prediction settings was conducted to apply the BPMF to the Statistics Online data. The results shows that the BPMF with using side information provided more accurate prediction in the performance of both existing and new students on items, compared to the algorithm without using any side information. When the data are sparse, it is demonstrated that a lower dimensional solution of the BPMF would benefit the prediction accuracy. Lastly, the applicability of the BPMF to the online educational systems were discussed in the context of educational assessment.Kim, J.; Park, JY.; Van Den Noortgate, W. (2020). On the Use of Bayesian Probabilistic Matrix Factorization for Predicting Student Performance in Online Learning Environments. En 6th International Conference on Higher Education Advances (HEAd'20). Editorial Universitat Politècnica de València. (30-05-2020):751-759. https://doi.org/10.4995/HEAd20.2020.11137OCS75175930-05-202

Online-Based Learning Problematics Between Needs, Readiness And Implications On The Purity Of Learning Outcomes

ABSTRACT Online-based learning has been long echoed, online-based learning methods also have been implemented in several developed countries. In Indonesia it's self this method was already known as PJJ (distance learning). Does online based learning a necessity for students and teachers, while students and parents do not fully understand technology. then how the readiness technological infrastructure which includes the internet network, smartphones and laptops to support this learning, from the school and students. With the great distance between students and teachers, could online-based learning methods produce purity of learning outcomes. This paper will use qualitative methods, where data will be generated through observation, interviews and information from print and electronic media. The purpose of this paper is to map the problem of online base learning. at least, where one side of this method must be applied because of the pandemic period, on the other hand this method will not run optimally when conditions are normal. Keywords: online based learning, needs, purity of learning outcome

Learning to Rank: Online Learning, Statistical Theory and Applications.

Learning to rank is a supervised machine learning problem, where the output space is the special structured space of emph{permutations}. Learning to rank has diverse application areas, spanning information retrieval, recommendation systems, computational biology and others. In this dissertation, we make contributions to some of the exciting directions of research in learning to rank. In the first part, we extend the classic, online perceptron algorithm for classification to learning to rank, giving a loss bound which is reminiscent of Novikoff's famous convergence theorem for classification. In the second part, we give strategies for learning ranking functions in an online setting, with a novel, feedback model, where feedback is restricted to labels of top ranked items. The second part of our work is divided into two sub-parts; one without side information and one with side information. In the third part, we provide novel generalization error bounds for algorithms applied to various Lipschitz and/or smooth ranking surrogates. In the last part, we apply ranking losses to learn policies for personalized advertisement recommendations, partially overcoming the problem of click sparsity. We conduct experiments on various simulated and commercial datasets, comparing our strategies with baseline strategies for online learning to rank and personalized advertisement recommendation.PhDStatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133334/1/sougata_1.pd

A case study for measuring informal learning in PLEs

The technological support for learning and teaching processes is constantly changing. Information and Communication Technologies (ICT) applied to education, cause changes that affect the way in which people learn. This application introduces new software systems and solutions to carry out teaching and learning activities. Connected to ICT application, the emergence of Web 2.0 and its use in learning contexts enables an online implementation of the student-centred learning paradigm. In addition, 2.0 trends provide “new” ways to exchange, making easier for informal learning to become patent. Given this context, open and user-centered learning environments are needed to integrate such kinds of tools and trends and are commonly described as Personal Learning Environments. Such environments coexist with the institutional learning management systems and they should interact and exchange information between them. This interaction would allow the assessment of what happens in the personal environment from the institutional side. This article describes a solution to make the interoperability possible between these systems. It is based on a set of interoperability scenarios and some components and communication channels. In order to test the solution it is implemented as a proof of concept and the scenarios are validated through several pilot experiences. In this article one of such scenarios and its evaluation experiment is described to conclude that functionalities from the institutional environments and the personal ones can be combined and it is possible to assess what happens in the activities based on them.Peer ReviewedPostprint (published version

From Bandits to Experts: On the Value of Side-Observations

We consider an adversarial online learning setting where a decision maker can choose an action in every stage of the game. In addition to observing the reward of the chosen action, the decision maker gets side observations on the reward he would have obtained had he chosen some of the other actions. The observation structure is encoded as a graph, where node i is linked to node j if sampling i provides information on the reward of j. This setting naturally interpolates between the well-known "experts" setting, where the decision maker can view all rewards, and the multi-armed bandits setting, where the decision maker can only view the reward of the chosen action. We develop practical algorithms with provable regret guarantees, which depend on non-trivial graph-theoretic properties of the information feedback structure. We also provide partially-matching lower bounds.Comment: Presented at the NIPS 2011 conferenc

Efektivitas Pembelajaran Daring Di Masa Pandemi Covid-19

The purpose of this study was to identify obtaining information about the obstacles to the online teaching and learning process at home as a result of the COVID-19 pandemic. This research is entitled "effectiveness of online learning during the covid19 pandemic" to find out how student innovations increase the effectiveness of online learning and create conducive learning, this research is a research that uses a descriptive method with a qualitative approach, data collection techniques and uses interviews with informants who come from from the student side, namely the university of uin ar - raniry, the determination of the informants using random sampling technique, in this study the respondents were 15 students at the uin ar - raniry university, the interview method used the semi-structured interview method, a list of questions prepared for the interview was developed based on the related literature, the results of this study there are students who still have obstacles in online learning activities, namely, lack of mastery of technology, additional internet quota fees, additional work to help parents, lack of socialization between students