The use of programming online judges (POJ) to support students acquiring programming skills is common nowadays because this type of software contains a large collection of programming exercises to be solved by students. A POJ not only provides exercises but also automates the code compilation and its evaluation process. A common problem that students face when using POJ is information overload, as choosing the right problem to solve can be quite frustrating due to the large number of problems offered. The integration of current POJs into e-learning systems such as Intelligent Tutoring Systems (ITSs) is hard because of the lack of necessary information in ITSs. Hence, the aim of this paper is to support students with the information overload problem by using a collaborative filtering recommendation approach that filters out programming problems suitable for students’ programming skills. It uses an enriched user-problem matrix that implies a better student role representation, facilitating the computation of closer neighborhoods and hence a more accurate recommendation. Additionally a novel data preprocessing step that manages anomalous users’ behaviors that could affect the recommendation generation is also integrated in the recommendation process. A case study is carried out on a POJ real dataset showing that the proposal outperforms other previous approaches.

A recommendation approach for programming online judges supported by data preprocessing techniques

Programming online judges (POJs) are autograders that have been increasingly used in introductory programming courses (also known as CS1) since these systems provide instantaneous and accurate feedback for learners’ codes solutions and reduce instructors’ workload in evaluating the assignments. Nonetheless, learners typically struggle to find problems in POJs that are adequate for their programming skills. A potential reason is that POJs present problems with varied categories and difficulty levels, which may cause a cognitive overload, due to the large amount of information (and choice) presented to the student. Thus, students can often feel less capable, which may result in undesirable affective states, such as frustration and demotivation, decreasing their performance and potentially leading to increasing dropout rates. Recently, new research emerged on systems to recommend problems in POJs; however, the data collection for these approaches was not fine-grained; importantly, they did not take into consideration the students’ previous effort and achievement. Thus, this study proposes for the first time a prescriptive analytics solution for students’ programming behaviour by constructing and evaluating an automatic recommender module based on students’ effort, to personalise the problems presented to the learner in POJs. The aim is to improve the learners achievement, whilst minimising negative affective states in CS1 courses. Results in a within-subject double-blind controlled experiment showed that our method significantly improved positive affective states, whilst minimising the negatives ones. Moreover, our recommender significantly increased students’ achievement (correct solutions) and reduced dropout and failure in problem-solving.

A Recommender System Based on Effort: Towards Minimising Negative Affects and Maximising Achievement in CS1 Learning.

The use of programming online judges (POJs) has risen dramatically in recent years, owing to the fact that the auto-evaluation of codes during practice motivates students to learn programming. Since POJs have greater number of programming problems in their repository, learners experience information overload. Recommender systems are a common solution to information overload. Current recommender systems used in e-learning platforms are inadequate for POJ since recommendations should consider learners’ current context, like learning goals and current skill level (topic knowledge and difficulty level). To overcome the issue, we propose a context-aware practice problem recommender system based on learners’ skill level navigation patterns. Our system initially performs skill level navigation pattern mining to discover frequent skill level navigations in the POJ and to find learners’ learning goals. Collaborative filtering (CF) and content-based filtering approaches are employed to recommend problems in the current and next skill levels based on frequent skill level navigation patterns. The sequence similarity measure is used to find the top k neighbors based on the sequence of problems solved by the learners. The experiment results based on the real-world POJ dataset show that our approach considering the learners’ current skill level and learning goals outperforms the other approaches in practice problem recommender systems. 

/pdf/context-aware-practice-problem-recommendation-using-learners-3hjki4q7.pdf

Context-Aware Practice Problem Recommendation Using Learners’ Skill Level Navigation Patterns

With the rapid development of the Internet, the transmission and acquisition of information is becoming more and more convenient, and the problem of information overload is becoming more and more serious. The birth of the recommendation system effectively alleviates the problem of information overload, which can recommend potential interests to the users according to the user characteristics and project characteristics of the system. So far, the recommendation system has been widely used in business, but it is still immature in the field of online learning. Time transfer as one of the important influencing factors of recommendation system, its influence mode still needs further research and experimental investigation. To solve this problem, we analyze the users' behaviors, and propose time migration model based on different behaviors of users: short-term behavior model and long-term behavior model. With the data of online learning system, modeling the relationship between learners, problems and learners-problems, we propose a knowledge recommendation algorithm based on time migration (KRBTM). Our approach involves four steps: (1) creating the time migration model, (2) adjusting the model of learners and learners-questions with time migration model, (3) computing ratings similarity of questions based on time migration model, (4) applying KRBTM to recommend the top-N questions to learners. In this paper, we test the time migration model on Movielens with a good result. We also apply KRBTM to the real datasets of TJU ACM-ICPC Online Judge from Tianjin University, and the experiments show that KRBTM has a higher recall and F value than traditional algorithms.

A Knowledge Recommendation Algorithm Based on Time Migration

Programming Online Judges (POJs) are tools that contain a large collection of programming problems to be solved by students as a component of their training and programming practices This contribution presents a recommendation approach to suggest to students the more suitable problems to solve for increasing their performance and motivation in POJs Some key features of the approach are the use of an enriched user-problem matrix that incorporates specific information related to the user performance in the POJ, and the development of a strategy for natural noise management in such a matrix The experimental evaluation shows the improvements of the proposal as compared to previous works

A Recommender System for Supporting Students in Programming Online Judges

The Research of the Recommendation Algorithm in Online Learning

With a great deal of digitized textual information now available on the internet, it is almost impossible for people to assimilate all the information timely. Therefore, the technologies of topic detection and tracking are used for constructing news topics from news stories in order to bring convenience to people. However, traditional topic detection methods are not always so effective in detecting emerging hot news topics in a short period of time, and most topic detection methods use single-pass clustering algorithm which is with low accuracy and very sensitive to the input sequence of news documents. In order to improve clustering accuracy, we utilize a temporal distance factor to segment timeline into equal parts and propose a novel two-times single-pass clustering algorithm to deal with news stories in each part of timeline separately. Moreover, the aging theory is combined with our approach to build life-span model of topics from which we can obtain variation trend of hotness value of topics. The results of experiments show that our approach is effective and the life circle model of topics established by our method can conform to reality well.

/pdf/online-hot-topic-detection-based-on-segmented-timeline-and-45d4yg5nez.pdf

Online Hot Topic Detection Based on Segmented Timeline and Aging Theory

Recommendation system, as an information filtering method, has been widely applied and the hybrid recommendation algorithm is by far the most commonly used technique. But in the field of online learning system, recommendation system is not mature enough. In this paper, we analyzed the Tianjin University Online Judge, established a model to apply the recommendation algorithm to it, and then proposed a label-based hybrid recommendation algorithm which is applicable to the system. Also, by conducting experiments on real users’ data of Tianjin University Online Judge System, we evaluated our algorithm and obtained good results.

http://www.joics.com/downloadpaper.aspx?id=3773&name=2015_12_6_2221_2228.pdf

Lable-based Hybrid Recommendation Algorithm: Implementation and Analysis

Bp-cf hybrid recommendation algorithm : implementation and analysis

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The Research of the Recommendation Algorithm in Online Learning

Online Hot Topic Detection Based on Segmented Timeline and Aging Theory

Lable-based Hybrid Recommendation Algorithm: Implementation and Analysis

Bp-cf hybrid recommendation algorithm : implementation and analysis