Decision-Based Grading Model System and Student Performance Analysis Using Rule-Based Algorithm

Rutgers Robert Wood Johnson Medical School.

English learning materials for young learners in Indonesia are often limited in interactivity and the integration of digital learning resources that are appropriate to the characteristics of student learning, especially in the context of extracurricular learning. This study aims to develop English learning materials by integrating the Duolingo App and to evaluate the suitability of the materials developed for use in the UNU Yogyakarta English Club program. This study applied the Research and Development (R&D) approach using the ADDIE model, consisting of Analysis, Design, Development, Implementation, and Evaluation stages. The need analysis data were taken from pre-test results involving 57 students enrolled in the English program, semi-structured interviews with parents, and expert judgment for material validation. The developed English learning materials were designed and developed into ten learning units integrating the Duolingo App, equipped with printable worksheets and interactive learning activities. Expert validation was conducted by one expert in learning materials and learning models across five aspects: content, design, motivation, and the feasibility of the developed materials. The expert validation yielded a score of 4.18, indicating that this learning material is good and suitable as a supporting medium for English language learning. The implementation stage involves applying the developed materials in actual classes, followed by evaluation through observation and analysis of student performance at the end of each learning session. The study’s findings show that the pedagogically developed material is appropriate for classroom use. Students showed positive responses in terms of engagement, motivation, and participation during the learning process.

University of Florida College of Medicine

An interlingual subtitling course for Welsh‐English language transfer has presented a novel challenge in the formulation of a teaching, learning and assessment strategy since the technique is bounded by cultural, contextual, technological and physiological factors. A course has been designed to provide professional capability and this has been developed over ten delivery cycles to include common language and cultural transfer difficulties with various translation strategies. Analysis of student performance on course indicates the need for regular practice to maintain capability and despite the many and varied constraints it is possible to develop an individual style.

Collaborative Filtering (CF) Recommendation System is a prominent technology which is widely online. Large variety of available CF algorithms and the multitude of their possible parameters have a huge impact on quality of the outcome on ECommerce. Unfortunately, the literature on CF recommender system evaluation presents different evaluation metrics at different situation and could not provide any suggestion about the best. At the same time, it is a fact to accept that the accuracy measure should be different for each CF algorithm and depends upon the classification accuracy of that particular algorithm. As an initiative to address this problem in the present research paper, predictive accuracy metrics, classification accuracy metrics and rank accuracy metrics are considered as the classification accuracy metrics to know the overall competence for significant features of the chosen CF algorithms. At this juncture, normalization, a distinctive evaluation methodology, has been adopted to accomplish unique evaluation results of recommender systems. In this research paper, different accuracy metrics assessment would be brought into a common scale by taking into consideration of normalization process to evaluate metrics of the CF algorithms. A comprehensive comparative analysis is carried out and tabulated.

In the European Higher Education Area (EHEA), official degree studies should promote the competences and knowledge of students. This work is based on active strategies (conceptual maps and discussion panels) where students analyse and summarize the topics proposed in class, to improve their knowledge and skills. Therefore, the objective of this paper is to analyse the performance of business students by using the mentioned strategies in two different groups during the academic year 2018-2019. The implementation of this research was carried out using a quantitative approach, with mixed models (SPSS v20 software) of three dependent variables (two of knowledge and one for competences). The conclusions indicate that the group that used the panel discussion methodology increases more the acquired competences and the understanding of the basic concepts that are taught on the subject, than the group of the conceptual maps strategy.

Recommender systems provide users with content they might be interested in. Conventionally, recommender systems are evaluated mostly by using prediction accuracy metrics only. But, the ultimate goal of a recommender system is to increase user satisfaction. Therefore, evaluations that measure user satisfaction should also be performed before deploying a recommender system in a real target environment. Such evaluations are laborious and complicated compared to the traditional, data-centric evaluations, though. In this study, we carried out a user-centric evaluation of state-of-the-art recommender systems as well as a graph-based approach in the ecologically valid setting of an authentic social learning platform. We also conducted a data-centric evaluation on the same data to investigate the added value of user-centric evaluations and how user satisfaction of a recommender system is related to its performance in terms of accuracy metrics. Our findings suggest that user-centric evaluation results are not necessarily in line with data-centric evaluation results. We conclude that the traditional evaluation of recommender systems in terms of prediction accuracy only does not suffice to judge performance of recommender systems on the user side. Moreover, the user-centric evaluation provides valuable insights in how candidate algorithms perform on each of the five quality metrics for recommendations: usefulness, accuracy, novelty, diversity, and serendipity.

Recently, several approaches to recommendation systems have been studied. A recommender system aims to provide personalized recommendations to users for specific items. Most of these systems always provide the most relevant items of users or items. Traditionally, the evaluation of recommender system quality has focused on the various predictive accuracy metrics of these. However, recommender system must be not only accurate but also useful to users. User satisfaction with recommender systems as an evaluation criterion of recommender system is related not only to how accurately the system recommends but also to how much it supports the user’s decision making. In particular, highly serendipitous recommendation would help a user to find a surprising and interesting item. Serendipity in this study is defined as a measure of the extent to which the recommended items are both attractive and surprising to the users. Therefore, this paper proposes an application of serendipity measure to recommender systems to improve the performance of recommender systems. In this study we define relevant or attractive unexpectedness as serendipity measure for assessing recommendation systems. That is, serendipity measure is evaluated as the measure indicating how the recommender system can find unexpected and useful items for users. Our experimental results show that highly serendipitous recommendation has better performance than the other recommendations in terms of recommendation accuracy.

Product development is a multi-faceted role that a growing number of engineers are tasked with. This represents a significant shift in career paths for those employed in the chemical and materials engineering disciplines, who typically were concerned with bulk commodity manufacturing. This paradigm shift requires the undergraduate curriculum to be adapted to prepare students for these new responsibilities. The authors present here on a product design capstone course developed for chemical engineering seniors at the University at Buffalo (UB), The State University of New York (SUNY). The course encompasses the following themes: a general framework for product design and development (identify customer needs, convert needs to specifications, create ideas/concepts, select concept, formulate/test/manufacture product; and (nano)structure-property relations that guide the search for smart/tunable/functional materials for contemporary needs and challenges. These two main themes are enriched with case studies of successful products. Students put the course material into practice by working through formulated product design projects that are drawn from real-world problems. The authors begin by presenting the course organization, teaching techniques, and assessment strategy. They then discuss examples of student work to show how students apply the course material to solve problems. Finally, they present an analysis of historical student performance in the course. The analysis seeks to identify correlation between related student deliverables, and also between the Product Design course and a prerequisite materials science and engineering course.

A recommender system uses specific algorithms and techniques in order to suggest specific services, goods or other type of recommendations that users could be interested in. User’s preferences or ratings are used as inputs and top-N recommendations are produced by the system. The evaluation of the recommendations is usually based on accuracy metrics such as the Mean Absolute Error (MAE) and the Root Mean Squared Error (RMSE), while on the other hand Precision and Recall is used to measure the quality of the top-N recommendations. Recommender systems development has been mainly focused in the development of new recommendation algorithms. However, one of the major problems in modern offline recommendation system is the sparsity of the datasets and the selection of the suitable users Y that could produce the best recommendations for users X. In this paper, we propose an algorithm that uses Fuzzy sets and Fuzzy norms in order to evaluate the correlation between users in the data set so the system can select and use only the most relevant users. At the same time, we are extending our previous work about Reproduction of experiments in recommender systems by developing new explanations and variables for the proposed new algorithm. Our proposed approach has been experimentally evaluated using a real dataset and the results show that it is really efficient and it can increase both accuracy and quality of recommendations.

Recommender systems have become increasingly accurate in suggesting content to users, resulting in users primarily consuming content through recommendations. This can cause the user's interest to narrow toward the recommended content, something we refer to as preference amplification. While this can contribute to increased engagement, it can also lead to negative experiences such as lack of diversity and echo chambers. We propose a theoretical framework for studying such amplification in a matrix factorization based recommender system. We model the dynamics of the system, where users interact with the recommender systems and gradually drift'' toward the recommended content, with the recommender system adapting, based on user feedback, to the updated preferences. We study the conditions under which preference amplification manifests, and validate our results with simulations. Finally, we evaluate mitigation strategies that prevent the adverse effects of preference amplification and present experimental results using a real-world large-scale video recommender system showing that by reducing exposure to potentially objectionable content we can increase user engagement by up to 2%.

With the increasing problem of information overload, recommenders are essential on many websites to help users find interesting content quickly. User feedback is the most valuable form of model feedback, and the form and how user feedback is used affects the accuracy, coverage and experience of recommendations. Thus, this paper reviews feedback types in recommender systems: explicit, implicit, and deliberate implicit feedback, and discusses their pros and cons in terms of accuracy, ease-of-use, and user effort. Additionally, it discusses challenges recommendation models face when using feedback, like data sparsity, bias reinforcement, and fairness limitations. Meanwhile, it proposes strategies to address these challenges by integrating multi-source feedback, fairness-aware modeling, and interpretability enhancement to boost user confidence. The results show gains in accuracy, coverage, and fairness. In addition, it provides exploratory analyses on dynamic feedback and cross-domain recommendations, offering insights for modeling and applying multi-type feedback in future recommender systems.

The emergence of e-learning as well as development of learning management systems (LMSs) provides teachers and students more interactive and convenient environment for courses. However, there are still gaps between the institutional reality and the design requirements of LMSs, which hinders adoption of them on existing courses. This paper proposes our course resource service platform SPCR for supporting interactive and flexible instructional process in courses and presents its modular design of system and applications in courses of the author. Based on the relationships between courses and students from teachers’ perspective, SPCR provides extensive functionalities such as courseware and learning material delivery, exchange and sharing mechanism for videos, quiz organization and homework management. Student performance analysis have demonstrated the feasibility and effectiveness of the SPCR platform, while feedback of students also expresses their willingness and engagement in using SPCR.

Student assessment in higher education is the systematic process of evaluating learners’ academic performance, skills and competencies to ensure quality learning outcomes and guide instructional improvement. This study investigates how teachers at Tribhuvan University perceive student assessments, exploring their experiences with various evaluation methods in higher education. It highlights the effectiveness and challenges teachers face during these processes. Utilizing a practitioner enquiry and phenomenological design of qualitative research approach, this study gathers data through in-depth interviews with four university teachers and focus group discussion with five students of higher education based on purposive sampling. It delves into teachers’ and students’ lived experiences regarding assessment practices. The findings not only shed light on the multifaceted perceptions of grading systems and assessment strategies but also offer valuable insights into enhancing the fairness and quality of student evaluation. The implications of this study are significant, offering recommendations informed by practitioner enquiry to guide policymakers, academic leaders and educators in refining student assessment practices, ultimately enhancing learning outcomes and enriching educational experiences.

Nowadays, right information and service access are the big challenge in the World Wide Web. There are number of tools available to access the right information in the market. Recommender system is the most valuable tool to provide such service. The applications of recommender systems include recommending movies, music, television programs, books, documents, Web sites, conferences, tourism scenic spots and learning materials, and involve the areas of e-commerce, e-learning, e-library, e-government, and e-business services. These recommender systems can be designed with different objectives, strategies, algorithms, and methods. This article discusses in detail about what is recommender system, needs, benefits, challenges, strategies, algorithms, and measures used for designing the recommender system. It also gives the details about the user personalization and customization.