A Perspective Of Automated Programming Error Feedback Approaches In Problem Solving Exercises

Programming tools are meant for student to practice programming. Automated programming error feedback will be provided for students to self-construct the knowledge through their own experience. This paper has clustered current approaches in providing automated error programming feedback to the students during problem solving exercises. These include additional syntax error messages, solution template mismatches, test data comparison, assisted agent report and collaborative comment feedback. The study is conducted based on published papers for last two decades. The trends are analyzed to get the overview of latest research contributions towards eliminating programming difficulties among students. The result shows that future direction of automated programming error feedback approaches may combine agent and collaborative feedback approaches towards more interactive, dynamic, end-user oriented and specific goal oriented. Such future direction may help other researchers fill in the gap on new ways of assisting learners to better understand feedback messages provided by automated assessment tool

\u27Friends Group\u27 in Recommender Systems: Effects of User Involvement in the Formation of Recommending Groups

How can we improve the acceptance of recommendations in collaborative systems? The group identity of recommenders and recipient involvement in group formation impacts on the likelihood that users of social collaborative systems would accept recommendations provided on it. We introduce the term \u27friends group\u27 to describe a sub-group of the \u27neighbors group\u27 in recommender systems that is not solely rank-dependent, as opposed to \u27neighbors\u27 that are assigned by rating similarity. The \u27friends group\u27 is unique because of the user\u27s involvement in its formation and the user\u27s ability to choose the characteristics of its members. The latter aspect corresponds to Festinger\u27s Social Comparison Theory , suggesting that \u27neighbors\u27 (like-minded groups) are relevant for \u27low-risk\u27 domains whereas similarity-based \u27friends\u27 are more relevant for \u27high-risk\u27 domains. We conducted a two year field study, using QSIA, a Web-based Java-programmed collaborative system for collection, management, sharing and assignment of learning knowledge items. QSIA was implemented in over ten courses in several universities. QSIA database and logs contained approximately 31,000 records of items-seeking acts, 3,000 users, 10,000 items, 3,000 rankings and knowledge items from 30 domains. We found that the difference between acceptance and rejection ratios of recommendations when the items originated from an advising group comprised of \u27friends\u27, is significantly higher than when the advising group is the more commonly known \u27neighbors group\u27. The difference increases for frequently recommended as opposed to other items and for experienced as opposed to \u27average\u27 users. Our longitudinal analysis indicates a positive learning curve for experienced users, who, over time, increasingly preferred \u27friends group\u27 over \u27neighbors group\u27 as their experience with the system increases. Also, users chose their own group to participate in the advising group significantly more than other groups. The contribution of this study is in explicating the relationship between the perceived quality of the recommendation (measured in terms of usage actions ), and the user\u27s involvement in the formation of the advising group. The major implication of our findings for the development of recommender systems is the need to enhance involvement of recommendation seekers in the process of forming the advising group. Developers of recommender systems should consider increasing users\u27 control over relevant characteristics of the members of this group

A Novel Adaptation Model for E-Learning Recommender Systems Based on Student’s Learning Style

In recent years, a substantial increase has been witnessed in the use of online learning resources by learn- ers. However, owing to an information overload, many find it difficult to retrieve appropriate learning resources for meeting learning requirements. Most of the existing systems for e-learning make use of a “one-size-fits-all” approach, thus providing all learners with the same content. Whilst recommender systems have scored notable success in the e-commerce domain, they still suffer from drawbacks in terms of making the right recommendations for learning resources. This can be attributed to the differences among learners’ preferences such as varying learning styles, knowledge levels and sequential learning patterns. Hence, to identify the needs of an individual student, e-learning systems that can build profiles of student preferences are required. In addition, changing students’ preferences and multidimensional attributes of the course content are not fully considered simultaneously. It is by failing to review these issues that existing recommendation algorithms often give inaccurate recommendations. This thesis focuses on student learning styles, with the aim of dynamically tailoring the learning process and course content to meet individual needs. The proposed Ubiquitous LEARNing (ULEARN) system is an adaptive e-learning recommender system geared towards providing a personalised learning environ- ment, which ensures that course learning objects are in line with the learner’s adaptive profile. This thesis delivers four main contributions: First, an innovative algorithm which dynamically reduces the number of questions in the Felder-Silverman Learning Styles (FSLSM) questionnaire for the purpose of initialising student profiles has been proposed. The second contribution comprises examining the accuracy of various similarity metrics so as to select the most suitable similarity measurements for learning objects recommendation algorithm. The third contribution includes an Enhanced Collaboration Filtering (ECF) algorithm and an Enhanced Content-Based Filtering (ECBF) algorithm, which solves the issues of cold-start and data sparsity in- herent to the traditional Collaborative Filtering (CF) and the traditional Content-based Filtering (CBF), respectively. Moreover, these two new algorithms have been combined to create a new Enhanced Hybrid Filtering (EHF) algorithm that recommends highly accurate personalised learning objects on the basis of the stu- dents’ learning styles. The fourth contribution is a new algorithm that tracks patterns of student learning behaviours and dynam- ically adapts the student learning style accordingly. The ULEARN recommendation system was implemented with Visual Studio in C++ and Windows Pre- sentation Foundation (WPF) for the development of the Graphical User Interface (GUI). The experimental results revealed that the proposed algorithms have achieved significant improvements in student’s profile adaptation and learning objects recommendation in contrast with strong benchmark models. Further find- ings from experiments indicated that ULEARN can provide relevant learning object recommendations based on students’ learning styles with the overall students’ satisfaction at almost 90%. Furthermore, the results showed that the proposed system is capable of mitigating the problems data sparsity and cold-start, thereby improving the accuracy and reliability of recommendation of the learning object. All in all, the ULEARN system is competent enough to support educational institutions in recommending personalised course content, improving students’ performance as well as promoting student engagement.Arab academy for science technology & maritime transpor

A review of the role of sensors in mobile context-aware recommendation systems

Recommendation systems are specialized in offering suggestions about specific items of different types (e.g., books, movies, restaurants, and hotels) that could be interesting for the user. They have attracted considerable research attention due to their benefits and also their commercial interest. Particularly, in recent years, the concept of context-aware recommendation system has appeared to emphasize the importance of considering the context of the situations in which the user is involved in order to provide more accurate recommendations. The detection of the context requires the use of sensors of different types, which measure different context variables. Despite the relevant role played by sensors in the development of context-aware recommendation systems, sensors and recommendation approaches are two fields usually studied independently. In this paper, we provide a survey on the use of sensors for recommendation systems. Our contribution can be seen from a double perspective. On the one hand, we overview existing techniques used to detect context factors that could be relevant for recommendation. On the other hand, we illustrate the interest of sensors by considering different recommendation use cases and scenarios

Video Recommendations Based on Visual Features Extracted with Deep Learning

Postponed access: the file will be accessible after 2022-06-01When a movie is uploaded to a movie Recommender System (e.g., YouTube), the system can exploit various forms of descriptive features (e.g., tags and genre) in order to generate personalized recommendation for users. However, there are situations where the descriptive features are missing or very limited and the system may fail to include such a movie in the recommendation list, known as Cold-start problem. This thesis investigates recommendation based on a novel form of content features, extracted from movies, in order to generate recommendation for users. Such features represent the visual aspects of movies, based on Deep Learning models, and hence, do not require any human annotation when extracted. The proposed technique has been evaluated in both offline and online evaluations using a large dataset of movies. The online evaluation has been carried out in a evaluation framework developed for this thesis. Results from the offline and online evaluation (N=150) show that automatically extracted visual features can mitigate the cold-start problem by generating recommendation with a superior quality compared to different baselines, including recommendation based on human-annotated features. The results also point to subtitles as a high-quality future source of automatically extracted features. The visual feature dataset, named DeepCineProp13K and the subtitle dataset, CineSub3K, as well as the proposed evaluation framework are all made openly available online in a designated Github repository.Masteroppgave i informasjonsvitenskapINFO390MASV-INF

Audio Processing and Loudness Estimation Algorithms with iOS Simulations

abstract: The processing power and storage capacity of portable devices have improved considerably over the past decade. This has motivated the implementation of sophisticated audio and other signal processing algorithms on such mobile devices. Of particular interest in this thesis is audio/speech processing based on perceptual criteria. Specifically, estimation of parameters from human auditory models, such as auditory patterns and loudness, involves computationally intensive operations which can strain device resources. Hence, strategies for implementing computationally efficient human auditory models for loudness estimation have been studied in this thesis. Existing algorithms for reducing computations in auditory pattern and loudness estimation have been examined and improved algorithms have been proposed to overcome limitations of these methods. In addition, real-time applications such as perceptual loudness estimation and loudness equalization using auditory models have also been implemented. A software implementation of loudness estimation on iOS devices is also reported in this thesis. In addition to the loudness estimation algorithms and software, in this thesis project we also created new illustrations of speech and audio processing concepts for research and education. As a result, a new suite of speech/audio DSP functions was developed and integrated as part of the award-winning educational iOS App 'iJDSP." These functions are described in detail in this thesis. Several enhancements in the architecture of the application have also been introduced for providing the supporting framework for speech/audio processing. Frame-by-frame processing and visualization functionalities have been developed to facilitate speech/audio processing. In addition, facilities for easy sound recording, processing and audio rendering have also been developed to provide students, practitioners and researchers with an enriched DSP simulation tool. Simulations and assessments have been also developed for use in classes and training of practitioners and students.Dissertation/ThesisM.S. Electrical Engineering 201