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Full-Text Articles in Social and Behavioral Sciences
Towards A More Effective Bidirectional Lstm-Based Learning Model For Human-Bacterium Protein-Protein Interactions, Huaming Chen, Jun Shen, Lei Wang, Yaochu Jin
Towards A More Effective Bidirectional Lstm-Based Learning Model For Human-Bacterium Protein-Protein Interactions, Huaming Chen, Jun Shen, Lei Wang, Yaochu Jin
Faculty of Engineering and Information Sciences - Papers: Part B
The identification of protein-protein interaction (PPI) is one of the most important tasks to understand the biological functions and disease mechanisms. Although numerous databases of biological interactions have been published in debt to advanced high-throughput technology, the study of inter-species protein-protein interactions, especially between human and bacterium pathogens, remains an active yet challenging topic to harness computational models tackling the complex analysis and prediction tasks. In this paper, we comprehensively revisit the prediction task of human-bacterium protein-protein interactions (HB-PPI), which is a first ever endeavour to report an empirical evaluation in learning and predicting HB-PPI based on machine learning models. …
Utilizing Qr Codes To Verify The Visual Fidelity Of Image Datasets For Machine Learning, Yang-Wai Chow, Willy Susilo, Jianfang Wang, Richard Buckland, Joon Sang Baek, Jongkil Kim, Nan Li
Utilizing Qr Codes To Verify The Visual Fidelity Of Image Datasets For Machine Learning, Yang-Wai Chow, Willy Susilo, Jianfang Wang, Richard Buckland, Joon Sang Baek, Jongkil Kim, Nan Li
Faculty of Engineering and Information Sciences - Papers: Part B
Machine learning is becoming increasingly popular in modern technology and has been adopted in various application areas. However, researchers have demonstrated that machine learning models are vulnerable to adversarial examples in their inputs, which has given rise to a field of research known as adversarial machine learning. Potential adversarial attacks include methods of poisoning datasets by perturbing input samples to mislead machine learning models into producing undesirable results. While such perturbations are often subtle and imperceptible from the perspective of a human, they can greatly affect the performance of machine learning models. This paper presents two methods of verifying the …
Automatic Ventricular Nuclear Magnetic Resonance Image Processing With Deep Learning, Binbin Yong, Chen Wang, Jun Shen, Fucun Li, Hang Yin
Automatic Ventricular Nuclear Magnetic Resonance Image Processing With Deep Learning, Binbin Yong, Chen Wang, Jun Shen, Fucun Li, Hang Yin
Faculty of Engineering and Information Sciences - Papers: Part A
Cardiovascular diseases (CVD) seriously threaten the health of human beings, and they have caused widespread concern in recent years. At present, the diagnosis of CVD is mainly conducted by computed tomography (CT), echocardiography and nuclear magnetic resonance (NMR) technologies. NMR imaging technology is widely used in medical applications owing to its characteristics of high resolution and very low radiation. However, manual NMR image segmentation is time-consuming and error-prone, which has led to the research on automatic NMR image segmentation technologies. Researchers tend to explore the ventricular NRM image segmentation to improve the accuracy of CVD diagnosis. In this study, based …
Using Cost-Sensitive Learning And Feature Selection Algorithms To Improve The Performance Of Imbalanced Classification, Fang Feng, Kuan-Ching Li, Jun Shen, Qingguo Zhou, Xuhui Yang
Using Cost-Sensitive Learning And Feature Selection Algorithms To Improve The Performance Of Imbalanced Classification, Fang Feng, Kuan-Ching Li, Jun Shen, Qingguo Zhou, Xuhui Yang
Faculty of Engineering and Information Sciences - Papers: Part A
Imbalanced data problem is widely present in network intrusion detection, spam filtering, biomedical engineering, finance, science, being a challenge in many real-life data-intensive applications. Classifier bias occurs when traditional classification algorithms are used to deal with imbalanced data. As already known, the General Vector Machine (GVM) algorithm has good generalization ability, though it does not work well for the imbalanced classification. Additionally, the state-of-the-art Binary Ant Lion Optimizer (BALO) algorithm has high exploitability and fast convergence rate. Based on these facts, we have proposed in this paper a Cost-sensitive Feature selection General Vector Machine (CFGVM) algorithm based on GVM and …
Hybrid Translation And Language Model For Micro Learning Material Recommendation, Jiayin Lin
Hybrid Translation And Language Model For Micro Learning Material Recommendation, Jiayin Lin
Faculty of Engineering and Information Sciences - Papers: Part A
As an emerging pedagogy, micro learning aims to make use of people’s fragmented spare time and provide personalized online learning service, for example, by pushing fragmented knowledge to specific learners. In the context of big data, the recommender system is the key factor for realizing the online personalization service, which significantly determines what information will be fmally accessed by the target learners. In the education discipline, due to the pedagogical requirements and the domain characteristics, ranking recommended learning materials is essential for maintaining the outcome of the massive learning scenario. However, many widely used recommendation strategies in other domains showed …
From Ideal To Reality: Segmentation, Annotation, And Recommendation, The Vital Trajectory Of Intelligent Micro Learning, Jiayin Lin, Geng Sun, Tingru Cui, Jun Shen, Dongming Xu, Ghassan Beydoun, Ping Yu, David Pritchard, Li Li, Shiping Chen
From Ideal To Reality: Segmentation, Annotation, And Recommendation, The Vital Trajectory Of Intelligent Micro Learning, Jiayin Lin, Geng Sun, Tingru Cui, Jun Shen, Dongming Xu, Ghassan Beydoun, Ping Yu, David Pritchard, Li Li, Shiping Chen
Faculty of Engineering and Information Sciences - Papers: Part B
The soaring development of Web technologies and mobile devices has blurred time-space boundaries of people’s daily activities. Such development together with the life-long learning requirement give birth to a new learning style, micro learning. Micro learning aims to effectively utilize learners’ fragmented time to carry out personalized learning activities through online education resources. The whole workflow of a micro learning system can be separated into three processing stages: micro learning material generation, learning materials annotation and personalized learning materials delivery. Our micro learning framework is firstly introduced in this paper from a higher perspective. Then we will review representative segmentation …
Apex2s: A Two-Layer Machine Learning Model For Discovery Of Host-Pathogen Protein-Protein Interactions On Cloud-Based Multiomics Data, Huaming Chen, Jun Shen, Lei Wang, Chi-Hung Chi
Apex2s: A Two-Layer Machine Learning Model For Discovery Of Host-Pathogen Protein-Protein Interactions On Cloud-Based Multiomics Data, Huaming Chen, Jun Shen, Lei Wang, Chi-Hung Chi
Faculty of Engineering and Information Sciences - Papers: Part B
No abstract provided.
Evolutionary Learner Profile Optimization Using Rare And Negative Association Rules For Micro Open Learning, Geng Sun, Jiayin Lin, Jun Shen, Tingru Cui, Dongming Xu, Huaming Chen
Evolutionary Learner Profile Optimization Using Rare And Negative Association Rules For Micro Open Learning, Geng Sun, Jiayin Lin, Jun Shen, Tingru Cui, Dongming Xu, Huaming Chen
Faculty of Engineering and Information Sciences - Papers: Part B
The actual data availability, readiness and publicity has slowed down the research of making use of computational intelligence to improve the knowledge delivery in an emerging learning mode, namely adaptive micro open learning, which naturally has high demand in quality and quantity of data to be fed. In this study, we contribute a novel approach to tackle the current scarcity of both data and rules in micro open learning, by adopting evolutionary algorithm to produce association rules with both rare and negative associations taken into account. These rules further drive the generation and optimization of learner profiles through refinement and …
Deep Sequence Labelling Model For Information Extraction In Micro Learning Service, Jiayin Lin, Zhexuan Zhou, Geng Sun, Jun Shen, David Pritchard, Tingru Cui, Dongming Xu, Li Li, Ghassan Beydoun
Deep Sequence Labelling Model For Information Extraction In Micro Learning Service, Jiayin Lin, Zhexuan Zhou, Geng Sun, Jun Shen, David Pritchard, Tingru Cui, Dongming Xu, Li Li, Ghassan Beydoun
Faculty of Engineering and Information Sciences - Papers: Part B
Micro learning aims to assist users in making good use of smaller chunks of spare time and provides an effective online learning service. However, to provide such personalized online services on the Web, a number of information overload challenges persist. Effectively and precisely mining and extracting valuable information from massive and redundant information is a significant preprocessing procedure for personalizing online services. In this study, we propose a deep sequence labelling model for locating, extracting, and classifying key information for micro learning services. The proposed model is general and combines the advantages of different types of classical neural network. Early …
Refinement And Augmentation For Data In Micro Learning Activity With An Evolutionary Rule Generators, Geng Sun, Jiayin Lin, Tingru Cui, Jun Shen, Dongming Xu, Mahesh Kayastha
Refinement And Augmentation For Data In Micro Learning Activity With An Evolutionary Rule Generators, Geng Sun, Jiayin Lin, Tingru Cui, Jun Shen, Dongming Xu, Mahesh Kayastha
Faculty of Engineering and Information Sciences - Papers: Part B
Improving both the quantity and quality of existing data are placed at the center of research for adaptive micro open learning. To cover this research gap, our work targets on the current scarcity of both data and rules that represent open learning activities. An evolutionary rule generator is constructed, which consists of an outer loop and an inner loop. The outer loop runs a genetic algorithm (GA) to produce association rules that can be effective in the micro open learning scenario from a small amount of available data sources; while the inner loop optimizes generated candidates by taking into account …
Ensemble Machine Learning Approaches For Webshell Detection In Internet Of Things Environments, Binbin Yong, Wei Wei, Kuan-Ching Li, Jun Shen, Qingguo Zhou, Marcin Wozniak, Dawid Polap, Robertas Damasevicius
Ensemble Machine Learning Approaches For Webshell Detection In Internet Of Things Environments, Binbin Yong, Wei Wei, Kuan-Ching Li, Jun Shen, Qingguo Zhou, Marcin Wozniak, Dawid Polap, Robertas Damasevicius
Faculty of Engineering and Information Sciences - Papers: Part B
The Internet of things (IoT), made up of a massive number of sensor devices interconnected, can be used for data exchange, intelligent identification, and management of interconnected “things.” IoT devices are proliferating and playing a crucial role in improving the living quality and living standard of the people. However, the real IoT is more vulnerable to attack by countless cyberattacks from the Internet, which may cause privacy data leakage, data tampering and also cause significant harm to society and individuals. Network security is essential in the IoT system, and Web injection is one of the most severe security problems, especially …
Cooperative Secondary Voltage Control Of Static Converters In A Microgrid Using Model-Free Reinforcement Learning, Edward Smith, Duane A. Robinson, Ashish P. Agalgaonkar
Cooperative Secondary Voltage Control Of Static Converters In A Microgrid Using Model-Free Reinforcement Learning, Edward Smith, Duane A. Robinson, Ashish P. Agalgaonkar
Faculty of Engineering and Information Sciences - Papers: Part B
Agent-based secondary voltage regulation in an islanded MicroGrid is complicated by non-linear system dynamics, state couplings and uncertain communication network topology information. This paper proposes an off-policy learning algorithm for cooperative secondary voltage control which can synthesize an optimal feedback controller in real-time without knowledge of the system model. A simulation model has been developed using MATLAB/Simulink, which demonstrates a working controller. Results from the simulations are included, and practical considerations regarding implementation on a real system discussed.
Mlaas: A Cloud-Based System For Delivering Adapative Micro Learning In Mobile Mooc Learning, Geng Sun, Tingru Cui, Jianming Yong, Jun Shen, Shiping Chen
Mlaas: A Cloud-Based System For Delivering Adapative Micro Learning In Mobile Mooc Learning, Geng Sun, Tingru Cui, Jianming Yong, Jun Shen, Shiping Chen
Faculty of Engineering and Information Sciences - Papers: Part B
No abstract provided.
Ontological Learner Profile Identification For Cold Start Problem In Micro Learning Resources Delivery, Geng Sun, Tingru Cui, Jun Shen, Dongming Xu, Ghassan Beydoun, Shiping Chen
Ontological Learner Profile Identification For Cold Start Problem In Micro Learning Resources Delivery, Geng Sun, Tingru Cui, Jun Shen, Dongming Xu, Ghassan Beydoun, Shiping Chen
Faculty of Engineering and Information Sciences - Papers: Part B
No abstract provided.
Vdes J2325-5229 A Z = 2.7 Gravitationally Lensed Quasar Discovered Using Morphology-Independent Supervised Machine Learning, Fernanda Ostrovski, Richard G. Mcmahon, Andrew J. Connolly, Cameron A. Lemon, Matthew W. Auger, Manda Banerji, Johnathan M. Hung, Sergey E. Koposov, Christopher E. Lidman
Vdes J2325-5229 A Z = 2.7 Gravitationally Lensed Quasar Discovered Using Morphology-Independent Supervised Machine Learning, Fernanda Ostrovski, Richard G. Mcmahon, Andrew J. Connolly, Cameron A. Lemon, Matthew W. Auger, Manda Banerji, Johnathan M. Hung, Sergey E. Koposov, Christopher E. Lidman
Faculty of Engineering and Information Sciences - Papers: Part B
We present the discovery and preliminary characterization of a gravitationally lensed quasar with a source redshift zs = 2.74 and image separation of 2.9 arcsec lensed by a foreground zl = 0.40 elliptical galaxy. Since optical observations of gravitationally lensed quasars show the lens system as a superposition of multiple point sources and a foreground lensing galaxy, we have developed a morphology-independent multi-wavelength approach to the photometric selection of lensed quasar candidates based on Gaussian Mixture Models (GMM) supervised machine learning. Using this technique and gi multicolour photometric observations from the Dark Energy Survey (DES), near-IR JK photometry …
A Framework Of Mlaas For Facilitating Adaptive Micro Learning Through Open Education Resources In Mobile Environment, Geng Sun, Tingru Cui, Wanwu Guo, Shiping Chen, Jun Shen
A Framework Of Mlaas For Facilitating Adaptive Micro Learning Through Open Education Resources In Mobile Environment, Geng Sun, Tingru Cui, Wanwu Guo, Shiping Chen, Jun Shen
Faculty of Engineering and Information Sciences - Papers: Part B
No abstract provided.
Sbar: A Conceptual Framework To Support Learning Path Adaptation In Mobile Learning, Alva Hendi Muhammad, Jun Shen, Ghassan Beydoun, Dongming Xu
Sbar: A Conceptual Framework To Support Learning Path Adaptation In Mobile Learning, Alva Hendi Muhammad, Jun Shen, Ghassan Beydoun, Dongming Xu
Faculty of Engineering and Information Sciences - Papers: Part B
No abstract provided.
A Comparison Study For Supervised Machine Learning Models In Cancer Classification, Huaming Chen, Hong Zhao, Lei Wang, Jiangning Song, Jun Shen
A Comparison Study For Supervised Machine Learning Models In Cancer Classification, Huaming Chen, Hong Zhao, Lei Wang, Jiangning Song, Jun Shen
Faculty of Engineering and Information Sciences - Papers: Part B
No abstract provided.
Student Rules: Exploring Patterns Of Students' Computer-Efficacy And Engagement With Digital Technologies In Learning, Sarah Katherine Howard, Jun Ma, Jie Yang
Student Rules: Exploring Patterns Of Students' Computer-Efficacy And Engagement With Digital Technologies In Learning, Sarah Katherine Howard, Jun Ma, Jie Yang
Faculty of Engineering and Information Sciences - Papers: Part A
Teachers' beliefs about students' engagement in and knowledge of digital technologies will affect technologically integrated learning designs. Over the past few decades, teachers have tended to feel that students were confident and engaged users of digital technologies, but there is a growing body of research challenging this assumption. Given this disparity, it is necessary to examine students' confidence and engagement using digital technologies to understand how differences may affect experiences in technologically integrated learning. However, the complexity of teaching and learning can make it difficult to isolate and study multiple factors and their effects. This paper proposes the use of …
Learning Path Adaptation In Online Learning Systems, Alva Hendi Muhammad, Qingguo Zhou, Ghassan Beydoun, Dongming Xu, Jun Shen
Learning Path Adaptation In Online Learning Systems, Alva Hendi Muhammad, Qingguo Zhou, Ghassan Beydoun, Dongming Xu, Jun Shen
Faculty of Engineering and Information Sciences - Papers: Part A
Learning path in online learning systems refers to a sequence of learning objects which are designated to help the students in improving their knowledge or skill in particular subjects or degree courses. In this paper, we review the recent research on learning path adaptation to pursue two goals, first is to organize and analyze the parameter of adaptation in learning path; the second is to discuss the challenges in implementing learning path adaptation. The survey covers the state of the art and aims at providing a comprehensive introduction to the learning path adaptation for researchers and practitioners.
Learning A Pose Lexicon For Semantic Action Recognition, Lijuan Zhou, Wanqing Li, Philip O. Ogunbona
Learning A Pose Lexicon For Semantic Action Recognition, Lijuan Zhou, Wanqing Li, Philip O. Ogunbona
Faculty of Engineering and Information Sciences - Papers: Part A
This paper presents a novel method for learning a pose lexicon comprising semantic poses defined by textual instructions and their associated visual poses defined by visual features. The proposed method simultaneously takes two input streams, semantic poses and visual pose candidates, and statistically learns a mapping between them to construct the lexicon. With the learned lexicon, action recognition can be cast as the problem of finding the maximum translation probability of a sequence of semantic poses given a stream of visual pose candidates. Experiments evaluating pre-trained and zero-shot action recognition conducted on MSRC-12 gesture and WorkoutSu-10 exercise datasets were used …
Rational And Self-Adaptive Evolutionary Extreme Learning Machine For Electricity Price Forecast, Chixin Xiao, Zhao Y. Dong, Yan Xu, Ke Meng, Xun Zhou, Xin Zhang
Rational And Self-Adaptive Evolutionary Extreme Learning Machine For Electricity Price Forecast, Chixin Xiao, Zhao Y. Dong, Yan Xu, Ke Meng, Xun Zhou, Xin Zhang
Faculty of Engineering and Information Sciences - Papers: Part A
Electricity price forecast is of great importance to electricity market participants. Given the sophisticated time-series of electricity price, various approaches of extreme learning machine (ELM) have been identified as effective prediction approaches. However, in high dimensional space, evolutionary extreme learning machine (E-ELM) is time-consuming and difficult to converge to optimal region when just relying on stochastic searching approaches. In the meanwhile, due to the complicated functional relationship, objective function of E-ELM seems difficult also to be mined directly for some useful mathematical information to guide the optimum exploring. This paper proposes a new differential evolution (DE) like algorithm to enhance …
Facilitating Student And Staff Engagement Across Multiple Offshore Campuses For Transnational Education Using An Immersive Video Augmented Learning Platform, Sasha Nikolic, Wanqing Li
Facilitating Student And Staff Engagement Across Multiple Offshore Campuses For Transnational Education Using An Immersive Video Augmented Learning Platform, Sasha Nikolic, Wanqing Li
Faculty of Engineering and Information Sciences - Papers: Part A
Opportunities in transnational education have been growing across the higher education sector. The incentive for institutions to explore opening offshore satellite campuses includes access to more students and building the institutions reputation across the globe. A number of risks are also associated with transnational education, especially in terms of quality. It is important that students across all campuses receive the same high standard of education. That is, students at offshore campuses should not be placed at a disadvantage compared to students studying at the institutions main campus. This paper explores the possibility of providing students from offshore campuses better access …
Learning Network Storage Curriculum With Experimental Case Based On Embedded Systems, Qingguo Zhou, Jiong Wu, Ting Wu, Jun Shen, Rui Zhou
Learning Network Storage Curriculum With Experimental Case Based On Embedded Systems, Qingguo Zhou, Jiong Wu, Ting Wu, Jun Shen, Rui Zhou
Faculty of Engineering and Information Sciences - Papers: Part A
In this paper, we present an experimental case for the course of "Network Storage and Security," which benefited from an improved learning outcome for our students. The newly designed experiments-based contents are merged into the current course to help students obtain practical experiences about network storage. The experiments aim to build a network storage system based on available resources instead of any specialized network storage equipment. Technically, students can learn general practical knowledge of network storage on iSCSI (a network storage protocol based on IP technology) and also the technologies of embedded system. Through the experimental case, we found that …
Relationship Between Learning In The Engineering Laboratory And Student Evaluations, Sasha Nikolic, Thomas F. Suesse, Thomas Goldfinch, Timothy J. Mccarthy
Relationship Between Learning In The Engineering Laboratory And Student Evaluations, Sasha Nikolic, Thomas F. Suesse, Thomas Goldfinch, Timothy J. Mccarthy
Faculty of Engineering and Information Sciences - Papers: Part A
BACKGROUND OR CONTEXT This study is built upon previous research that developed an instrument to measure the learning objectives of the laboratory across the cognitive, psychomotor and affective domains with research that investigated student evaluations of sessional laboratory demonstrators, laboratory experiments and facilities. This research highlighted the importance of laboratory work in engineering education, and the need to improve our understanding of how learning occurs in the laboratory. PURPOSE OR GOAL Student evaluations are heavily used in higher education, and a greater understanding is needed on how these evaluations relate to learning. APPROACH An instrument used to measure learning in …
Improving The Laboratory Learning Experience: A Process To Train And Manage Teaching Assistants, Sasha Nikolic, Peter J. Vial, Montserrat Ros, David Stirling, Christian H. Ritz
Improving The Laboratory Learning Experience: A Process To Train And Manage Teaching Assistants, Sasha Nikolic, Peter J. Vial, Montserrat Ros, David Stirling, Christian H. Ritz
Faculty of Engineering and Information Sciences - Papers: Part A
This paper describes in detail a successful training program developed for sessional (part-time or nonpermanent) laboratory demonstrators employed in the Electrical Engineering Department of an Australian university. Such demonstrators play an important role in teaching practical concepts and skills in engineering. The success of the program relies on a centralized approach coordinated by a carefully selected Laboratory Manager responsible for the recruitment, allocation, training, and development of sessional teachers, and for assessing student satisfaction with them. The paper examines the overall impact of the program on these teachers': 1) introducing laboratory material; 2) preparation; 3) communication; 4) interest in student …
Using Online And Multimedia Resources To Enhance The Student Learning Experience In A Telecommunications Laboratory Within An Australian University, Peter J. Vial, Sasha Nikolic, Montserrat Ros, David Stirling, Parviz Doulai
Using Online And Multimedia Resources To Enhance The Student Learning Experience In A Telecommunications Laboratory Within An Australian University, Peter J. Vial, Sasha Nikolic, Montserrat Ros, David Stirling, Parviz Doulai
Faculty of Engineering and Information Sciences - Papers: Part A
A laboratory component of an undergraduate telecommunications course consistently scored poorly for student learning experience on student surveys at an Australian university. Consultation with experienced academic staff revealed the need to modify the teaching resources available for the laboratory to include web-based multimedia and interactive resources. This new material was developed and made available to students and teaching staff in early 2011 via an Australian university e-learning package which was used to deliver the subject. The students and demonstrators were then encouraged to use this new resource to prepare for the three hour laboratory sessions. Surveys of students who took …
Towards Bringing Adaptive Micro Learning Into Mooc Courses, Geng Sun, Tingru Cui, Kuanching Li, Dongming Xu, Shiping Chen, Jun Shen, Wanwu Guo
Towards Bringing Adaptive Micro Learning Into Mooc Courses, Geng Sun, Tingru Cui, Kuanching Li, Dongming Xu, Shiping Chen, Jun Shen, Wanwu Guo
Faculty of Engineering and Information Sciences - Papers: Part A
In this paper we illustrate a proposal with regard to providing learners adaptive micro learning experiences, which can be fulfilled within fragmented time pieces. The framework of our system is demonstrated, while it aims to deliver customized micro learning contents taking into account learners’ specific demands, learning styles, preference and context.
Mlaas: A Cloud System For Mobile Micro Learning In Mooc, Geng Sun, Tingru Cui, Shiping Chen, Wanwu Guo, Jun Shen
Mlaas: A Cloud System For Mobile Micro Learning In Mooc, Geng Sun, Tingru Cui, Shiping Chen, Wanwu Guo, Jun Shen
Faculty of Engineering and Information Sciences - Papers: Part A
No abstract provided.
Drawing Micro Learning Into Mooc: Using Fragmented Pieces Of Time To Enable Effective Entire Course Learning Experiences, Geng Sun, Tingru Cui, Jianming Yong, Jun Shen, Shiping Chen
Drawing Micro Learning Into Mooc: Using Fragmented Pieces Of Time To Enable Effective Entire Course Learning Experiences, Geng Sun, Tingru Cui, Jianming Yong, Jun Shen, Shiping Chen
Faculty of Engineering and Information Sciences - Papers: Part A
Recently the massive open online course (MOOC) is an emerging trend that attracts many educators’ and researchers’ attentions. Based on our pilot study focusing on the development and operation of MOOC in Australia, we found MOOC is featured with mastery learning and blended learning, but it suffers from low completion rates. Brining micro learning into MOOC can be a feasible solution to improve current MOOC delivery and learning experience. We design a system which aims to provide adaptive micro learning contents as well as learning path identifications customized for each individual learner. To investigate how micro learning can impact learning …