A heuristic approach for new-item cold start problem in recommendation of micro open education resources

© Springer International Publishing AG, part of Springer Nature 2018. The recommendation of micro Open Education Resources (OERs) suffers from the new-item cold start problem because little is known about the continuously published micro OERs. This paper provides a heuristic approach to inserting newly published micro OERs into established learning paths, to enhance the possibilities of new items to be discovered and appear in the recommendation lists. It considers the accumulation and attenuation of user interests and conform with the demand of fast response in online computation. Performance of this approach has been proved by empirical studies

학습자 동기 부여 지원을 위한 MOOCs 인터페이스 개발

학위논문(석사) -- 서울대학교대학원 : 사범대학 교육학과(교육공학전공), 2022.2. Young Hoan Cho.With the development of information and communication technology (ICT), many people get educated not only in traditional classrooms but also online nowadays. As one way of online education, the market of MOOCs (Massive Open Online Courses) has been growing continuously since the first platform was opened to the public in 2012. Today in 2021, the number of MOOCs learners has reached up to 220 million, and MOOCs are playing an irreplaceable role in higher education, lifelong education, corporate education, etc. Although we can expect that MOOCs will become increasingly important in the field of education, with its rapid growth during the last decade, some issues of it have been exposed. One of the issues is the low completion rate. Compared to traditional education, MOOCs learners are reported more likely to drop out, which leads to the average completion rate at around 10%. According to previous studies, one of the reasons that causes this phenomenon is lacking motivation. As a part that interacts directly with users of an application, interface is crucial because it offers affordance and determines the way users use the application. And the interface becomes even more important when it comes to E-learning because motivators, which can affect learners' motivation, can be designed in the user interface. However, studies have shown that the current interface design of MOOCs lacks motivation factors and fails to facilitate interactive communication among MOOCs learners. Therefore, in this research, a MOOCs interface that focuses on improving learners' motivation was designed. To achieve this goal, the research questions considered were: 1) What are the interface design guidelines and interface functions to motivate MOOCs learners to sustain their learning? 2) What is the interface to motivate MOOCs learners to sustain their learning? and 3) What are the learners' responses to the interface? To answer the research questions, the type 1 design and development methodology proposed by Richey and Klein was followed. First, MOOCs interface design guidelines were derived by literature review and followed by 2 rounds of expert review conducted by 4 experts to ensure the internal validity. Second, a prototype of MOOCs interface was designed based on the guidelines by using prototyping tool Figma. Third, the prototype was given to 5 learners along with a series of tasks for learner response tests to ensure the external validity of the design guidelines, and based on the result, both the prototype and the guidelines were revised. The final version of the MOOCs interface design guidelines consists of 3 motivational design principles （Autonomy, Competence, and Relatedness）, 12 motivational design guidelines (5 autonomy-supported, 4 competence-supported, and 3 relatedness-supported) along with 34 design guidelines developed for MOOCs interface. Based on these design guidelines, the functions of the MOOCs interface in this research were designed. Based on the autonomy-supported guidelines, functions such as learning mode selection (self-paced, scheduled, premiere), learning group, learning activity, goal setting, dashboard, reminder, recommendation, and feedback were designed. And based on the competence-supported guidelines, functions such as account register, course enrollment, learning path, team activity support, dashboard, and goal setting were designed. Meanwhile, based on the relatedness-supported guidelines, functions such as dashboard, feedback, keyword checklist, learning group, chatting window, group/team activity, course evaluation, team assignment, mind map, and note were designed. The participating learners were satisfied with the design. The survey data showed that learners’ general perceptions of the MOOCs interface reached 4.44, perceived autonomy reached 4.40, perceived competence reached 4.52, and perceived relatedness reached 4.66 (5 points Likert scale). The in-depth interview data was open coded into three categories: 1) Advantages of the MOOCs interface, 2) Problems with the MOOCs interface, and 3) Suggestions for improvement. The advantages include providing choices for autonomy support, providing scaffolding and adaptive learning for competence support, providing interactive learning for relatedness support, and providing novel meanwhile helpful functions that existing platforms don’t have. The problems include lacking tutorials for novel functions, inconsistent icons and choice of words, and improper positioning and interaction. The suggestions for improvement include adding the wiki function, adding the reminder function, and visualizing the timetable. The significance of this research can be summarized as follows: 1) proposed an intrinsic motivation oriented MOOCs interface. 2) introduced three learning modes to the MOOCs learning environment. 3) introduced the learning group and learning team to the MOOCs environment to facilitate learners’ interaction. 4) provided an example of the dashboard for the context of MOOCs. And 5) provided insight into how to help learners achieve personalized learning in the MOOCs environment.정보통신기술(ICT)의 발달로 오늘날 많은 사람이 전통적인 교실에서뿐만 아니라 온라인으로도 교육을 받고 있다. 온라인 교육의 한 방법으로 MOOC(Massive Open Online Courses)의 시장은 2012년 첫 플랫폼이 공개된 이후 지속적으로 성장하고 있으며, 교육학자에게 많은 관심을 받고 있다. 특히 MOOC는 고등교육, 평생교육, 기업교육 등에서 대체할 수 없는 역할을 한다는 연구 결과가 속속히 나오고 있다. 교육 분야에서 MOOC가 점점 더 중요해질 것이라고 예상되지만, 지난 10년 동안 MOOC의 급속한 성장과 함께 MOOC의 문제점들이 일부 발견되었다. 그중 하나는 낮은 이수율로서, 기존 교육에 비해 MOOC 학습자는 평균 이수율이 약 10%에 머무를 정도로 중도 탈락할 가능성이 높은 것으로 보고되었다. 선행 연구에 따르면 이러한 현상을 일으키는 원인 중 하나는 부족한 동기부여 때문이다. 사용자 인터페이스는 애플리케이션에서 사용자와 직접 상호 작용하는 부분으로서 어포던스를 제공하고, 사용자가 애플리케이션을 사용하는 방식을 결정하기 때문에 매우 중요하다고 말할 수 있다. 특히, E-learning 상황에서는 인터페이스가 더욱 중요한데, 이는 동기 부여에 영향을 미칠 수 있는 동기 요소들이 사용자 인터페이스에 설계될 수 있기 때문이다. 그러나 선행 연구에 따르면 현재의 MOOC 인터페이스 디자인은 동기 요소가 부족하고 MOOC 학습자 간의 상호 작용을 촉진하지 못하고 있다. 이에 따라서 본 연구에서는 학습자의 동기부여에 초점을 맞추어 MOOC 인터페이스를 설계하였고, 이를 위해 다음의 연구 문제들을 고려하였다. 1) MOOC 학습자가 학습을 지속하도록 동기를 부여하는 인터페이스 디자인 가이드라인은 무엇인가? 2) 인터페이스 디자인 가이드라인에 따른 예시적인 인터페이스는 무엇인가? 3) 인터페이스에 대한 학습자의 반응은 무엇인가? 연구 질문에 답하기 위해 Richey와 Klein이 제안한 설계ㆍ개발 방법론 중 유형 1을 따랐다. 먼저 문헌검토를 통해 MOOC 인터페이스 디자인 가이드라인을 도출하고, 내적 타당성을 확보하기 위해 4명의 전문가가 2차례에 걸쳐 전문가검토를 실시하였다. 다음으로, 프로토타이핑 도구인 Figma를 사용하여 가이드라인에 따라 MOOC 인터페이스의 프로토타입을 개발했다. 마지막으로, 디자인 가이드라인의 외적 타당성을 확보하기 위해 2차례에 걸쳐 사용성 평가를 하고 학습자의 반응을 측정했다. 일련의 과제와 함께 프로토타입을 5명의 학습자에게 제공하고, 그 결과를 바탕으로 프로토타입과 가이드라인을 모두 수정했다. MOOC 인터페이스 디자인 가이드라인의 최종 버전은 3가지 동기 부여 디자인 원리 (자율성, 유능성 및 관계성), 12가지 동기 부여 디자인 가이드라인 (5개 자율성 지원, 4개 유능성 지원, 3개 관계성 지원)과 34개의 MOOC 인터페이스 디자인 가이드라인으로 구성되었다. 본 연구에서 개발한 MOOC 인터페이스는 이러한 MOOC 인터페이스 디자인 가이드라인에 따른 기능을 포함하고 있다. 자율성 지원 가이드라인을 반영하는 기능으로 학습 모드 선택 (self-paced, scheduled, premiere), 학습 그룹, 학습활동 선택, 학습목표 설정, 대시보드, 리마인더, 추천, 피드백 등 기능이 있었고, 유능성 지원 가이드라인을 반영하는 기능으로 레지스터, 수업 등록, 학습 경로, 팀 활동 지원, 대시보드, 학습목표 설정 등 기능이 있었으며, 관계성 지원 가이드라인을 반영하는 기능으로 대시보드, 피드백, 키워드 체크리스트, 학습 그룹, 채팅창, 그룹/팀 활동, 수업 평가, 팀 과제, 마인드맵, 노트 등 기능이 있었다. 사용성 평가에 참여한 학습자들은 개발한 MOOCs 인터페이스에 만족했으며, 5점 척도 구성한 설문 문항으로 조사한 결과, 인터페이스 전반에 대한 인식 4.44점, 자율성에 대한 인식 4.40점, 유능성에 대한 인식 4.52점, 관계성에 대한 인식 4.66점이었다. 그리고 심층 인터뷰에서 취득한 질적 데이터를 오픈 코딩을 통해서 정리한 결과, 개발한 MOOCs 인터페이스의 장점, 문제점, 개선점을 도출했다. 장점으로 자율성 지원을 위한 선택 제공, 유능성 지원을 위한 스캐폴딩과 적응형 학습, 관계성 지원을 위해서 제공하는 상호작용, 그리고 기존 플랫폼과의 의미있는 차이점 등이 있었다. 문제점으로 기존 플랫폼들이 제공하지 않은 새로운 기능에 대한 가이드가 필요하다는 점, 불일치한 아이콘과 용어, 부적절한 포지셔닝과 인터랙션 등이 있었다. 개선점으로 위키 기능, 알림 메시지 기능을 추가하고 시간표를 시각화 등이 있었다. 본 연구의 의의는 다음과 같이 요약될 수 있다. 1) 내재적 동기부여 지향한 MOOC 인터페이스를 제안하였다. 2) MOOC 학습 환경에 3가지 학습 모드를 도입했다. 3) 학습자의 상호 작용을 촉진하기 위해 학습 그룹과 학습 팀을 MOOC 학습 환경에 도입했다. 4) MOOC 상황에 대시보드의 예시를 제공했다. 그리고 5) 학습자가 MOOC 환경에서 맞춤형 학습을 달성하도록 돕는 방법에 대한 인사이트를 제공했다.Chapter 1. Introduction 1 1.1. Problem Statement 1 1.2. Purpose of the Research 4 1.3. Research Questions 8 1.4. Definition of Terms 9 1.4.1. MOOCs 9 1.4.2. Motivation 11 1.4.3. User Interface 12 Chapter 2. Literature Review 14 2.1. MOOCs 14 2.1.1. Characteristics and Meaning 14 2.1.2. History of MOOCs 16 2.1.3. MOOCs Platforms and MOOCs Learners 20 2.1.4. The Critiques and Drop Out Phenomenon 26 2.2. Motivation 35 2.2.1. Motivation in Learning 35 2.2.2. Motivation Theories 37 2.3.1. User Interface Design & Interface Design for Education 45 2.3.2 Motivation Supported User Interface Design 48 Chapter 3. Research Method 54 3.1. Design and Development Methodology 54 3.2. Research Participants 58 3.3. Research Tools 60 3.3.1. Internal Validation Tools 60 3.3.2. Prototyping Tool 60 3.3.3. External Validation Tools 62 3.4. Data Collection and Analysis 65 3.4.1. Expert Review 65 3.4.2. Learners' Responses 66 Chapter 4. Findings 68 4.1. The MOOCs Interface Design Guidelines 68 4.1.1. The Final Version of the MOOCs Interface Design Guidelines 69 4.1.2. The Results of the Expert Review 80 4.2. Prototype of the MOOCs Interface 85 4.3. Learners' Responses to the MOOCs Interface 118 4.3.1. Learners' Response to the MOOCs Interface (First Usability Test) 119 4.3.2. Learners' Responses to the Revised Interface (Second Usability Test) 143 Chapter 5. Discussion and Conclusion 145 5.1. Discussion 146 5.2. Conclusion 151 Reference 154 APPENDIX 1 175 APPENDIX 2 189 APPENDIX 3 197 APPENDIX 4 206 APPENDIX 5 213 APPENDIX 6 220 국문초록 224석

Improving M-Learners\u27 Performance through Deep Learning Techniques by Leveraging Features Weights

© 2013 IEEE. Mobile learning (M-learning) has gained tremendous attention in the educational environment in the past decade. For effective M-learning, it is important to create an efficient M-learning model that can identify the exact requirements of mobile learners (M-learners). M-learning model is composed of features that are generated during M-learners\u27 interaction with mobile devices. For an adaptive M-learning model, not only learning features are required, but it is also important to determine how they differ for various M-learners, their weights, and interrelationship. This study proposes a robust and adaptive M-learning model that is based on machine learning and deep learning (ML/DL) techniques. The proposed M-learning model dynamically explores learning features, their corresponding weights, and association for M-learners. Based on learning features, the M-learning model categorizes M-learners into different performance groups. The M-learning model then provides adaptive content, suggestions, and recommendations to M-learners in order to make learning adaptive and stimulating. For comparative analysis, the prediction accuracy of five baseline ML models was compared with the deep Artificial Neural Network (deep ANN). The results demonstrated that deep ANN and Random Forest (RF) models exhibited better prediction accuracy. Subsequently, both models were selected for developing the M-learning model which included the performance categorization of M-learners under a five-level classification scheme and assigning weights to various features for providing adaptive help and support to M-learners. Our explanatory analysis has shown that behavioral features besides contextual features also influence the learning performance of M-learners. As a direct outcome of this research, more efficient, interactive, and useful mobile learning applications can be developed that accurately predict learning objectives and requirements of diverse M-learners thus helping M-learners in enhancing their study behavior

Annotation of multimedia learning materials for semantic search

Multimedia is the main source for online learning materials, such as videos, slides and textbooks, and its size is growing with the popularity of online programs offered by Universities and Massive Open Online Courses (MOOCs). The increasing amount of multimedia learning resources available online makes it very challenging to browse through the materials or find where a specific concept of interest is covered. To enable semantic search on the lecture materials, their content must be annotated and indexed. Manual annotation of learning materials such as videos is tedious and cannot be envisioned for the growing quantity of online materials. One of the most commonly used methods for learning video annotation is to index the video, based on the transcript obtained from translating the audio track of the video into text. Existing speech to text translators require extensive training especially for non-native English speakers and are known to have low accuracy. This dissertation proposes to index the slides, based on the keywords. The keywords extracted from the textbook index and the presentation slides are the basis of the indexing scheme. Two types of lecture videos are generally used (i.e., classroom recording using a regular camera or slide presentation screen captures using specific software) and their quality varies widely. The screen capture videos, have generally a good quality and sometimes come with metadata. But often, metadata is not reliable and hence image processing techniques are used to segment the videos. Since the learning videos have a static background of slide, it is challenging to detect the shot boundaries. Comparative analysis of the state of the art techniques to determine best feature descriptors suitable for detecting transitions in a learning video is presented in this dissertation. The videos are indexed with keywords obtained from slides and a correspondence is established by segmenting the video temporally using feature descriptors to match and align the video segments with the presentation slides converted into images. The classroom recordings using regular video cameras often have poor illumination with objects partially or totally occluded. For such videos, slide localization techniques based on segmentation and heuristics is presented to improve the accuracy of the transition detection. A region prioritized ranking mechanism is proposed that integrates the keyword location in the presentation into the ranking of the slides when searching for a slide that covers a given keyword. This helps in getting the most relevant results first. With the increasing size of course materials gathered online, a user looking to understand a given concept can get overwhelmed. The standard way of learning and the concept of “one size fits all” is no longer the best way to learn for millennials. Personalized concept recommendation is presented according to the user’s background knowledge. Finally, the contributions of this dissertation have been integrated into the Ultimate Course Search (UCS), a tool for an effective search of course materials. UCS integrates presentation, lecture videos and textbook content into a single platform with topic based search capabilities and easy navigation of lecture materials

Contributions to affective learning through the use of data analysis, visualizations and recommender sytems

Student modeling is an important issue in telematics learning environments, e.g. learning resources can be adapted based on the students' information. An emergent area of student modeling is the inclusion of affective information. The improvement of emotion detectors based on the students' events in different telematics learning environments is an open issue. Moreover, there is a need of proposing and evaluating new visualizations involving affective information, and proposing generic solutions for the recommendation of learning materials based on the affective information. This PhD proposes two different models for the detection of emotions in two different telematics learning environments. The first model uses a Hidden Markov Model to infer the emotions in a programming learning environment in which students should use different tools to learn how to program. The second model uses a set of rules to infer the emotions in a Massive Open Online Course platform in which students should solve exercises and watch videos. An evaluation of the first model for the detection of emotions was performed using a controlled experiment, comparing the results of the model with the students' answers regarding their emotions in different instants of times. The results showed that the model was not able to detect accurately the students' answers regarding their emotions. Other models of the literature applied in other learning environments were tested and they were not able to predict accurately the students' answers regarding their emotions. Therefore, the detection of emotions based on students' events in these types of environments might not be feasible, or the reference data of students' answers to a survey with different questions about emotions should be redefined. Moreover, this PhD proposes a set of affective-related visualizations for learning environments. Some of these visualizations only involve affective information, while others combine this affective information with other related to the students' activities with the learning platforms. Some of these visualizations were evaluated with real students and results showed a good usability, usefulness and effectiveness. Finally, this work proposes a generic framework for enabling the recommendation of learning resources based on affective information. The solution includes an Application Programming Interface for the definition of the different possible events. A specific implementation of this recommender has been developed as a plugin of the ROLE SDK platform.Programa Oficial de Doctorado en Ingeniería TelemáticaPresidente: Carlos Enrique Palau Salvador.- Secretario: Eva María Méndez Rodríguez, Eva Maria.- Vocal: Ruth Cobos Pére

Improving and Scaling Mobile Learning via Emotion and Cognitive-state Aware Interfaces

Massive Open Online Courses (MOOCs) provide high-quality learning materials at low cost to millions of learners. Current MOOC designs, however, have minimal learner-instructor communication channels. This limitation restricts MOOCs from addressing major challenges: low retention rates, frequent distractions, and little personalization in instruction. Previous work enriched learner-instructor communication with physiological signals but was not scalable because of the additional hardware requirement. Large MOOC providers, such as Coursera, have released mobile apps providing more flexibility with “on-the-go” learning environments. This thesis reports an iterative process for the design of mobile intelligent interfaces that can run on unmodified smartphones, implicitly sense multiple modalities from learners, infer learner emotions and cognitive states, and intervene to provide gains in learning. The first part of this research explores the usage of photoplethysmogram (PPG) signals collected implicitly on the back-camera of unmodified smartphones. I explore different deep neural networks, DeepHeart, to improve the accuracy (+2.2%) and robustness of heart rate sensing from noisy PPG signals. The second project, AttentiveLearner, infers mind-wandering events via the collected PPG signals at a performance comparable to systems relying on dedicated physiological sensors (Kappa = 0.22). By leveraging the fine-grained cognitive states, the third project, AttentiveReview, achieves significant (+17.4%) learning gains by providing personalized interventions based on learners’ perceived difficulty. The latter part of this research adds real-time facial analysis from the front camera in addition to the PPG sensing from the back camera. AttentiveLearner2 achieves more robust emotion inference (average accuracy = 84.4%) in mobile MOOC learning. According to a longitudinal study with 28 subjects for three weeks, AttentiveReview2, with the multimodal sensing component, improves learning gain by 28.0% with high usability ratings (average System Usability Scale = 80.5). Finally, I show that technologies in this dissertation not only benefit MOOC learning, but also other emerging areas such as computational advertising and behavior targeting. AttentiveVideo, building on top of the sensing architecture in AttentiveLearner2, quantifies emotional responses to mobile video advertisements. In a 24-participant study, AttentiveVideo achieved good accuracy on a wide range of emotional measures (best accuracy = 82.6% across 9 measures)

A Self-Regulated Learning Approach to Educational Recommender Design

Recommender systems, or recommenders, are information filtering systems prevalent today in many fields. One type of recommender found in the field of education, the educational recommender, is a key component of adaptive learning solutions as these systems avoid “one-size-fits-all” approaches by tailoring the learning process to the needs of individual learners. To function, these systems utilize learning analytics in a student-facing manner. While existing research has shown promise and explores a variety of types of educational recommenders, there is currently a lack of research that ties educational theory to the design and implementation of these systems. The theory considered here, self-regulated learning, is underexplored in educational recommender research. Self-regulated learning advocates a cyclical feedback loop that focuses on putting students in control of their learning with consideration for activities such as goal setting, selection of learning strategies, and monitoring of one’s performance. The goal of this research is to explore how best to build a self-regulated learning guided educational recommender and discover its influence on academic success. This research applies a design science methodology in the creation of a novel educational recommender framework with a theoretical base in self-regulated learning. Guided by existing research, it advocates for a hybrid recommender approach consisting of knowledge-based and collaborative filtering, made possible by supporting ontologies that represent the learner, learning objects, and learner actions. This research also incorporates existing Information Systems (IS) theory in the evaluation, drawing further connections between these systems and the field of IS. The self-regulated learning-based recommender framework is evaluated in a higher education environment via a web-based demonstration in several case study instances using mixed-method analysis to determine this approach’s fit and perceived impact on academic success. Results indicate that the self-regulated learning-based approach demonstrated a technology fit that was positively related to student academic performance while student comments illuminated many advantages to this approach, such as its ability to focus and support various studying efforts. In addition to contributing to the field of IS research by delivering an innovative framework and demonstration, this research also results in self-regulated learning-based educational recommender design principles that serve to guide both future researchers and practitioners in IS and education

Developing sustainable business models for institutions’ provision of open educational resources: Learning from OpenLearn users’ motivations and experiences

Universities across the globe have, for some time, been exploring the possibilities for achieving public benefit and generating business and visibility through releasing and sharing open educational resources (OER). Many have written about the need to develop sustainable and profitable business models around the production and release of OER. Downes (2006), for example, has questioned the financial sustainability of OER production at scale. Many of the proposed business models focus on OER’s value in generating revenue and detractors of OER have questioned whether they are in competition with formal education. This paper reports on a study intended to broaden the conversation about OER business models to consider the motivations and experiences of OER users as the basis for making a better informed decision about whether OER and formal learning are competitive or complementary with each other. The study focused on OpenLearn - the Open University’s (OU) web-based platform for OER, which hosts hundreds of online courses and videos and is accessed by over 3,000,000 users a year. A large scale survey and follow-up interviews with OpenLearn users worldwide revealed that university provided OER can offer learners a bridge to formal education, allowing them to try out a subject before registering on a formal course and to build confidence in their abilities as learners. In addition, it was found that using OER during formal paid-for study can improve learners’ performance and self-reliance, leading to increased retention and satisfaction with the learning experience

Open educational resources for all? Comparing user motivations and characteristics across The Open University’s iTunes U channel and OpenLearn platform.

With the rise in access to mobile multimedia devices, educational institutions have exploited the iTunes U platform as an additional channel to provide free educational resources with the aim of profile-raising and breaking down barriers to education. For those prepared to invest in content preparation, it is possible to produce interactive, portable material that can be made available globally. Commentators have questioned both the financial implications for platform-specific content production, and the availability of devices for learners to access it (Osborne, 2012). The Open University (OU) makes its free educational resources available on iTunes U and via its web-based open educational resources (OER) platform, OpenLearn. The OU’s OER on iTunes U reached the 60 million download mark in 2013; its OpenLearn platform boasts 27 million unique visitors since 2006. This paper reports the results of a large-scale study of users of the OU’s iTunes U channel and OpenLearn platform. A survey of several thousand users revealed key differences in demographics between those accessing OER via the web and via iTunes U. In addition, the data allowed comparison between three groups: formal learners, informal learners and educators. The study raises questions about whether university-provided OER meet the needs of users and makes recommendations for how content can be modified to suit their needs. As the publishing of OER becomes core to business, we reflect on reasons why understanding users’ motivations and demographics is vital, allowing for needs-led resource provision and content that is adapted to best achieve learner satisfaction, and to deliver institutions’ social mission

Automatically learning topics and difficulty levels of problems in online judge systems

Online Judge (OJ) systems have been widely used in many areas, including programming, mathematical problems solving, and job interviews. Unlike other online learning systems, such as Massive Open Online Course, most OJ systems are designed for self-directed learning without the intervention of teachers. Also, in most OJ systems, problems are simply listed in volumes and there is no clear organization of them by topics or difficulty levels. As such, problems in the same volume are mixed in terms of topics or difficulty levels. By analyzing large-scale users’ learning traces, we observe that there are two major learning modes (or patterns). Users either practice problems in a sequential manner from the same volume regardless of their topics or they attempt problems about the same topic, which may spread across multiple volumes. Our observation is consistent with the findings in classic educational psychology. Based on our observation, we propose a novel two-mode Markov topic model to automatically detect the topics of online problems by jointly characterizing the two learning modes. For further predicting the difficulty level of online problems, we propose a competition-based expertise model using the learned topic information. Extensive experiments on three large OJ datasets have demonstrated the effectiveness of our approach in three different tasks, including skill topic extraction, expertise competition prediction and problem recommendation