Course recommendation of MOOC with big data support: A contextual online learning approach

Hou, Yifan; Zhou, Pan; Xu, Jie; Wu, Dapeng

doi:10.1109/infcomw.2018.8406936

Cited by 28 publications

(25 citation statements)

References 11 publications

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“…The research problem described in this work is about Educational Data Mining (EDM), which is a technology for mining potential information from massive learners' behavior data, and it has been widely applied in scientific research, business, finance, and other fields [5]. There are many applications of EDM in the education field, such as building learners' feature model [6] and recommending courses or learning paths to students according to their learning behaviors [7]. The purpose of a majority of methods is to improve students' academic performance and promote their well-rounded development.…”

Section: Introductionmentioning

confidence: 99%

Student Performance Prediction with Short-Term Sequential Campus Behaviors

Wang

Guo

et al. 2020

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As students’ behaviors are important factors that can reflect their learning styles and living habits on campus, extracting useful features of them plays a helpful role in understanding the students’ learning process, which is an important step towards personalized education. Recently, the task of predicting students’ performance from their campus behaviors has aroused the researchers’ attention. However, existing studies mainly focus on extracting statistical features manually from the pre-stored data, resulting in hysteresis in predicting students’ achievement and finding out their problems. Furthermore, due to the limited representation capability of these manually extracted features, they can only understand the students’ behaviors shallowly. To make the prediction process timely and automatically, we treat the performance prediction task as a short-term sequence prediction problem, and propose a two-stage classification framework, i.e., Sequence-based Performance Classifier (SPC), which consists of a sequence encoder and a classic data mining classifier. More specifically, to deeply discover the sequential features from students’ campus behaviors, we first introduce an attention-based Hybrid Recurrent Neural Network (HRNN) to encode their recent behaviors by giving a higher weight to the ones that are related to the students’ last action. Then, to conduct student performance prediction, we further involve these learned features to the classic Support Vector Machine (SVM) algorithm and finally achieve our SPC model. We conduct extensive experiments in the real-world student card dataset. The experimental results demonstrate the superiority of our proposed method in terms of Accuracy and Recall.

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Section: Introductionmentioning

confidence: 99%

Student Performance Prediction with Short-Term Sequential Campus Behaviors

Wang

Guo

et al. 2020

Information

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“…For specific course recommendation of MOCC, some approaches such as collaborative filtering, content-based filtering and hybrid recommendation systems can be found in [113]- [115], [116]. The authors in [113] proposed a systematic methodology for recommending personalized courses and considering the sequence of learning curriculum. In their system, they considered a measurable context space with Lipschitz condition, where space is divided into many subspaces to represent different types of students.…”

Section: Recommendation Systemsmentioning

confidence: 99%

Big Educational Data & Analytics: Survey, Architecture and Challenges

Ang

Ge²,

Seng

2020

IEEE Access

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The proliferation of mobile devices and the rapid development of information and communication technologies (ICT) have seen increasingly large volume and variety of data being generated at an unprecedented pace. Big data have started to demonstrate significant values in higher education. This paper gives several contributions to the state-of-the-art for Big data in higher education and learning technologies research. Currently, there is no comprehensive survey or literature review for Big educational data. Most literature reviews from a few authors have focused on one of these fields: educational mining, learning analytics with discussions on one or two aspects such as Big data technologies without educational focus, social media data in education, etc. Most of these literature reviews are short and insufficient to provide more inclusive reviews for Big educational data. In this paper, we present a comprehensive literature review of the current and emerging paradigms for Big educational data. The survey is presented in five parts: (1) The first part presents an overview and classification of Big education research to show the full landscape in this field, which also gives a concise summary of the overall scope of this paper; (2) The second part presents a discussion for the various data sources from education platforms or systems including learning management systems (LMS), massive open online courses (MOOC), learning object repository (LOR), OpenCourseWare (OCW), open educational resources (OER), social media, linked data and mobile learning contributing to Big education data; (3) The third part presents the data collection, data mining and databases in Big education data; (4) The fourth part presents the technological aspects including Big data platforms and architectures such as Hadoop, Spark, Samza and Big data tools for Big education data; and (5) The fifth part presents different approaches of data analytics for Big education data. This part provides a more inclusive discussion on data analytics which is beyond traditional forms of learning analysis in higher education. This includes predictive analytics, learning analytics including collaborative, behavior, personal learnings and assessment, followed by recommendation systems, graph analytics, visual analytics, immersive learning and analytics, etc. The final part of the paper discusses social (e.g. privacy and ethical issues) and technological challenges for Big data in education. This part also illustrates the technological challenges faced by giving an example for utilizing graph-based analytics for a cross-institution learning analytics scenario. INDEX TERMS Big data, learning technologies, educational data, learning analytics.

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“…The parking lot monitoring system is investigated for end-to-end parking application to predict the parking availability in [21]. [12] proposed a course recommendation system where the most promising course according to students is suggested to increase the online course completion rate.…”

Section: Related Workmentioning

confidence: 99%

“…We further utilize the knowledge to facilitate reduced volume and share it through the VKN for better traffic planning and safer driving. Purpose Knowledge Method(s) Application [13] Resource Utilization Network Selection Optimization Access Network Selection [24] Network Management Optimal Links and Paths Optimization UAV Network Management [23] Understanding Human Mobility Socioeconomic Activities Clustering Urban Area Planning [10] Caching Utilization Content Election Machine Learning Edge Caching [21] Parking Lot Monitoring Parking Availability Machine Learning End-to-End Parking [12] Increasing Online Course Completion Rate The Most Promising Course Machine Learning Course Recommendation…”

Section: Introductionmentioning

confidence: 99%

Vehicular knowledge networking and application to risk reasoning

Uçar

Higuchi

Wang

et al. 2020

Proceedings of the Twenty-First International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Netw

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Vehicles are expected to generate and consume an increasing amount of data, but how to perform risk reasoning over relevant data is still not yet solved. Location, time of day and driver behavior change the risk dynamically and make risk assessment challenging. This paper introduces a new paradigm, transferring information from raw sensed data to knowledge and explores the knowledge of risk reasoning through vehicular maneuver conflicts. In particular, we conduct a simulation study to analyze the driving data and extract the knowledge of risky road users and risky locations. We use knowledge to facilitate reduced volume and share it through a Vehicular Knowledge Network (VKN) for better traffic planning and safer driving. CCS CONCEPTS • Computing methodologies → Reasoning about belief and knowledge; • Networks → Ad hoc networks; Cloud computing.

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Course recommendation of MOOC with big data support: A contextual online learning approach

Cited by 28 publications

References 11 publications

Student Performance Prediction with Short-Term Sequential Campus Behaviors

Student Performance Prediction with Short-Term Sequential Campus Behaviors

Big Educational Data & Analytics: Survey, Architecture and Challenges

Vehicular knowledge networking and application to risk reasoning

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