Extracting student patterns from log file Moodle course: A case study

This paper introduces a set of extracted factors from Moodle log file of the selected course as a case study that aims to capture student Engagement (E), Behavior (B), Personality (Pers) and Performance (P). The factors are applied to identify students’ EBPersP with different course activities. The data set used in this paper was selected from the "Introduction to Computer Science" online course that captures 273,906 records as a log file for 29 students, delivered in Spring 2020. The paper also tries to show whether there is a relationship between student engagement, behavior and personality and their performance. Results show different patterns of students’ interactions with course contents, activities, and assessments. Specifically, our findings highlight that students' EBPersP could be extracted from Moodle log files. In addition, the extracted factors could assist instructors on how to focus more on students with low and average performance, giving them more attention to enhancing their performance.

INTEGRATING KANO MODEL WITH DATA MINING TECHNIQUES TO ENHANCE CUSTOMER SATISFACTION

The business world is becoming more competitive from time to time; therefore, businesses are forced to improve their strategies in every single aspect. So, determining the elements that contribute to the clients\u27 contentment is one of the critical needs of businesses to develop successful products in the market. The Kano model is one of the models that help determine which features must be included in a product or service to improve customer satisfaction. The model focuses on highlighting the most relevant attributes of a product or service along with customers’ estimation of how these attributes can be used to predict satisfaction with specific services or products. This research aims at developing a method to integrate the Kano model and data mining approaches to select relevant attributes that drive customer satisfaction, with a specific focus on higher education. The significant contribution of this research is to improve the quality of United Arab Emirates University academic support and development services provided to their students by solving the problem of selecting features that are not methodically correlated to customer satisfaction, which could reduce the risk of investing in features that could ultimately be irrelevant to enhancing customer satisfaction. Questionnaire data were collected from 646 students from United Arab Emirates University. The experiment suggests that Extreme Gradient Boosting Regression can produce the best results for this kind of problem. Based on the integration of the Kano model and the feature selection method, the number of features used to predict customer satisfaction is minimized to four features. It was found that either Chi-Square or Analysis of Variance (ANOVA) features selection model’s integration with the Kano model giving higher values of Pearson correlation coefficient and R2. Moreover, the prediction was made using union features between the Kano model\u27s most important features and the most frequent features among 8 clusters. It shows high-performance results

Enabling the Development and Implementation of Digital Twins : Proceedings of the 20th International Conference on Construction Applications of Virtual Reality

Welcome to the 20th International Conference on Construction Applications of Virtual Reality (CONVR 2020). This year we are meeting on-line due to the current Coronavirus pandemic. The overarching theme for CONVR2020 is "Enabling the development and implementation of Digital Twins". CONVR is one of the world-leading conferences in the areas of virtual reality, augmented reality and building information modelling. Each year, more than 100 participants from all around the globe meet to discuss and exchange the latest developments and applications of virtual technologies in the architectural, engineering, construction and operation industry (AECO). The conference is also known for having a unique blend of participants from both academia and industry. This year, with all the difficulties of replicating a real face to face meetings, we are carefully planning the conference to ensure that all participants have a perfect experience. We have a group of leading keynote speakers from industry and academia who are covering up to date hot topics and are enthusiastic and keen to share their knowledge with you. CONVR participants are very loyal to the conference and have attended most of the editions over the last eighteen editions. This year we are welcoming numerous first timers and we aim to help them make the most of the conference by introducing them to other participants

Optimizing E-Management Using Web Data Mining

Today, one of the biggest challenges that E-management systems face is the explosive growth of operating data and to use this data to enhance services. Web usage mining has emerged as an important technique to provide useful management information from user's Web data. One of the areas where such information is needed is the Web-based academic digital libraries. A digital library (D-library) is an information resource system to store resources in digital format and provide access to users through the network. Academic libraries offer a huge amount of information resources, these information resources overwhelm students and makes it difficult for them to access to relevant information. Proposed solutions to alleviate this issue emphasize the need to build Web recommender systems that make it possible to offer each student with a list of resources that they would be interested in. Collaborative filtering is the most successful technique used to offer recommendations to users. Collaborative filtering provides recommendations according to the user relevance feedback that tells the system their preferences. Most recent work on D-library recommender systems uses explicit feedback. Explicit feedback requires students to rate resources which make the recommendation process not realistic because few students are willing to provide their interests explicitly. Thus, collaborative filtering suffers from “data sparsity” problem. In response to this problem, the study proposed a Web usage mining framework to alleviate the sparsity problem. The framework incorporates clustering mining technique and usage data in the recommendation process. Students perform different actions on D-library, in this study five different actions are identified, including printing, downloading, bookmarking, reading, and viewing the abstract. These actions provide the system with large quantities of implicit feedback data. The proposed framework also utilizes clustering data mining approach to reduce the sparsity problem. Furthermore, generating recommendations based on clusters produce better results because students belonging to the same cluster usually have similar interests. The proposed framework is divided into two main components: off-line and online components. The off-line component is comprised of two stages: data pre-processing and the derivation of student clusters. The online component is comprised of two stages: building student's profile and generating recommendations. The second stage consists of three steps, in the first step the target student profile is classified to the closest cluster profile using the cosine similarity measure. In the second phase, the Pearson correlation coefficient method is used to select the most similar students to the target student from the chosen cluster to serve as a source of prediction. Finally, a top-list of resources is presented. Using the Book-Crossing dataset the effectiveness of the proposed framework was evaluated based on sparsity level, and Mean Absolute Error (MAE) regarding accuracy. The proposed framework reduced the sparsity level between (0.07% and 26.71%) in the sub-matrices, whereas the sparsity level is between 99.79% and 78.81% using the proposed framework, and 99.86% (for the original matrix) before applying the proposed framework. The experimental results indicated that by using the proposed framework the performance is as much as 13.12% better than clustering-only explicit feedback data, and 21.14% better than the standard K Nearest Neighbours method. The overall results show that the proposed framework can alleviate the Sparsity problem resulting in improving the accuracy of the recommendations

An activity guide for: special needs, home economics education

The South Carolina Department of Education, Home Economics Education published this guide for home economics educators with strategies and activities for special needs learning

Automated Learner Classification Through Interface Event Stream And Summary Statistics Analysis

Reading comprehension is predominately measured through multiple choice examinations. Yet, as we will discuss in this thesis, such exams are often criticized for their inaccuracies. With the advent of big data and the rise of ITS (Intelligent Tutoring Systems), increasing focus will be placed on finding dynamic, automated ways of measuring students\u27 aptitude and progress. This work takes the first step towards automated learner classification based on the application of graphic organizers. We address the following specific problem experimentally: How effectively can we measure task comprehension via human translation of written text into a visual representation on a computer? Can an algorithm employ data from user interface (UI) interaction during the problem solving process, to classify the user\u27s abilities? Specifically, from the data we show machine learning predictions of what a human expert would say about the: 1. integrity of the visual representation produced; 2. level of logical problem solving strategy the user applies to the exercise; 3. level of effort the user gives to the exercise. The core of the experiment is a software system that allows a human subject to read a preselected text and then draw a diagram by manipulating icons on a grid-canvas using standard transforms

A Hydropower Facility as an Energy Water Signal Processor

In recent times, various efforts have been made to address the challenge of adequately representing hydropower systems in modeling frameworks, accounting for the lack of data to represent the multiple constraints in hydropower operation. This research is a pilot data-driven methodology for characterizing, classifying, and comparing the water-to-energy and energy-to-water signal transformations that hydropower facilities as signal processors accomplish. In this study, a Box Jenkins transfer function/noise model is used to identify the relationship between reservoir inflows and outflows. For examining the feasibility of this methodology, 5-minute fleet data for five storage and five run-of-river facilities was provided by the Tennessee Valley Authority (TVA) and transfer function models are developed. The influence of past inflow and outflow values on the current outflow decisions was investigated and summarized by examining the results of Box Jenkins methodology. Finally, dominance analysis was introduced to add value to the Box Jenkins model results and provide different stakeholders with a set of concepts to convey the functionality of hydropower