OFMTutor: An operator function model intelligent tutoring system

The design, implementation, and evaluation of an Operator Function Model intelligent tutoring system (OFMTutor) is presented. OFMTutor is intended to provide intelligent tutoring in the context of complex dynamic systems for which an operator function model (OFM) can be constructed. The human operator's role in such complex, dynamic, and highly automated systems is that of a supervisory controller whose primary responsibilities are routine monitoring and fine-tuning of system parameters and occasional compensation for system abnormalities. The automated systems must support the human operator. One potentially useful form of support is the use of intelligent tutoring systems to teach the operator about the system and how to function within that system. Previous research on intelligent tutoring systems (ITS) is considered. The proposed design for OFMTutor is presented, and an experimental evaluation is described

ADO-Tutor: Intelligent Tutoring System for leaning ADO.NET

This paper describes an Intelligent Tutoring System for helping users with ADO.NET called ADO-Tutor. The Intelligent Tutoring System was designed and developed using (ITSB) authoring tool for building intelligent educational systems. The user learns through the intelligent tutoring system ADO.NET, the technology used by Microsoft.NET to connect to databases. The material includes lessons, examples, and questions. Through the feedback provided by the intelligent tutoring system, the user's understanding of the material is assessed, and accordingly can be guided to different difficulty level of exercises and/or the lessons. The Intelligent Tutoring System was evaluated by a group of users and the results were more than satisfactory in terms of the quality of the material and the design of the system

Predicting student performance using data mining and learning analysis technique in Libyan Higher Education

The Technology has an increasing impact on all areas of life, including the education sector, and requires developing countries to emulate developed countries and integrate technology into their education systems. Recently schools in Libya are facing an issue trying to figure out why students perform poorly in certain subjects and how can they know how they will perform next in the future in coming semesters in perspective subject. There are several methods proposed to predict the student’s performance, using data mining techniques. In this paper, there are plans to create Data Mining Techniques in Education (i.e., DME) prediction model clustering, classification and association rule mining in many universities and schools in order to provide students and teachers with the most advanced platform. Although relatively late, the Libyan government finally responded to this challenge by investing heavily in rebuilding the education system and launching a national plan to presented method in terms of predicting students’ performance based on their grades in Math and English. The results are divided in to three main sections clustering analysis using k-mean algorithm, classification analysis was done using two rounds first using Gain Ratio Evaluations to find out the top attributes that used by J84 algorithm in second round of classification, and rule association analysis using A priori algorithm. Rule association analysis is applied for the clusters generate by clustering analysis to generate the rules associated with each cluster. For each section, a list of inputs is presented with the scale used for the values followed by the results of the algorithm and explanation for the finding

التنبؤ بأداء الطلاب بناء على ملف الطالب الأكاديمي

Data mining is an important field; it has been widely used in different domains. Oneof the fields that make use of data mining is Educational Data Mining. In this study, we apply machine learning models on data obtained from Palestine Technical University-Kadoorie (PTUK) in Tulkarm for students in the department of computer engineering and applied computing. Students in both fields study the same major courses; C++ and Java. Therefore, we focused on these courses to predict student’s performance. The goal of our study is predicting students’ performance measured by (GPA) in the major. There are many techniques that are used in the educational data mining field. We applied three models on the obtained data which have been commonly used in the educational data mining field; the decision tree with information gain measure, the decision tree with Gini index measure, and the naive Bayes model. We used these models inour work because they are efficient and they have a high speed in data classification, and prediction. The results suggest that the decision tree with information gain measure outperforms other models with 0.66 accuracy. We had a deeper look on key features that we train our models; precisely, their branch of study at school, field of study in the university, and whether or not the students have a scholarship. These features have an influence on the pre-diction. For example, the accuracy of the decision tree with information gain measure increases to 0.71 when applied on the subset of students who studied in the scientific branch at high school. This study is important for both the students and the higher management of PTUK. The university will be able to do some predictions on the performance of the students. In the carried experiments, the prediction of the model was in line with the actual expectation

Utilizing the Educational Data Mining Techniques Orange Technology for Detecting Patterns and Predicting Academic Performance of University Students

This study aimed at detecting the educational patterns and predicting the academic performance of university students through the “Orange” technology for data mining. To achieve this aim, a set of electronic courses taught to King Khalid University students through the Blackboard Learning Management System were selected. For knowledge detection, the K-Means clustering algorithm was used to detect patterns, while Linear Regression, Random Forest, KNN, Tree, SVM algorithms were used to predict students academic performance. The results indicated that the K- Means aggregation algorithm collected students scores in three main layers: the highest was in the first and second classes, while the lowest was in the third layer. As for predicting academic performance, the results indicated that students academic performance can be predicted through activities and quarterly tests for all courses except for one Course in which academic performance can be predicted through the semester tests only, and the quarterly activities do not contribute to predicting the students academic performance. The Linear Regression algorithm is the most contributing algorithm in predicting the academic performance of students, while the SVM algorithm is the least

Systematic mapping review on student’s performance analysis using big data predictive model

This paper classify the various existing predicting models that are used for monitoring andimproving students’ performance at schools and higher learning institutions. It analyses all theareas within the educational data mining methodology. Two databases were chosen for thisstudy and a systematic mapping study was performed. Due to the very infant stage of thisresearch area, only 114 articles published from 2012 till 2016 were identified. Within this, atotal of 59 articles were reviewed and classified. There is an increased interest and research inthe area of educational data mining, particularly in improving students’ performance withvarious predictive and prescriptive models. Most of the models are devised for pedagogicalimprovements ultimately. It is a huge scarcity in producing portable predictive models that fitsinto any educational environment. There is more research needed in the educational big data.Keywords: predictive analysis; student’s performance; big data; big data analytics; datamining; systematic mapping study

Navigating Ethics in Digital Humanities: A Deep Dive into Decision Distribution within Higher Education at the University of Jordan

This research aims to aid higher education institutions in making decisions that align with student needs and enhance their satisfaction. It considers decision presentation, timing of implementation, and communication of the benefits tied to educational quality improvements. To gauge student opinions, an online questionnaire research design was adopted, involving 3,000 male and female students from the University of Jordan. Findings indicated that students generally express dissatisfaction with higher education decisions and regulations due to unclear communication and limited implementation time. For predicting educational quality outcomes, four machine learning algorithms were employed, each corresponding to four different higher education decisions. Notably, the Random Forest (RF) algorithm showcased superior performance. In the initial questionnaire, it achieved an accuracy of 97%, which slightly decreased to 92% in the second questionnaire due to the expanded dataset and varying factors affecting accuracy. The k-Nearest Neighbors (KNN) algorithm also yielded impressive results, achieving a remarkable 94% accuracy in the third questionnaire. In the third questionnaire, the Decision Tree (DT) algorithm exhibited an accuracy of 85% in optimal scenarios. In contrast, the Convolutional Neural Network (CNN) algorithm, tailored for intricate tasks with numerous variables, consistently performed below expectations across all questionnaires. Its efficacy consistently lagged alternative algorithms, indicating a misalignment with the specific demands of its operational framework

Bayesian neural network learning for repeat purchase modelling in direct marketing.

We focus on purchase incidence modelling for a European direct mail company. Response models based on statistical and neural network techniques are contrasted. The evidence framework of MacKay is used as an example implementation of Bayesian neural network learning, a method that is fairly robust with respect to problems typically encountered when implementing neural networks. The automatic relevance determination (ARD) method, an integrated feature of this framework, allows to assess the relative importance of the inputs. The basic response models use operationalisations of the traditionally discussed Recency, Frequency and Monetary (RFM) predictor categories. In a second experiment, the RFM response framework is enriched by the inclusion of other (non-RFM) customer profiling predictors. We contribute to the literature by providing experimental evidence that: (1) Bayesian neural networks offer a viable alternative for purchase incidence modelling; (2) a combined use of all three RFM predictor categories is advocated by the ARD method; (3) the inclusion of non-RFM variables allows to significantly augment the predictive power of the constructed RFM classifiers; (4) this rise is mainly attributed to the inclusion of customer\slash company interaction variables and a variable measuring whether a customer uses the credit facilities of the direct mailing company.Marketing; Companies; Models; Model; Problems; Neural networks; Networks; Variables; Credit;

Learning Computer Networks Using Intelligent Tutoring System

Intelligent Tutoring Systems (ITS) has a wide influence on the exchange rate, education, health, training, and educational programs. In this paper we describe an intelligent tutoring system that helps student study computer networks. The current ITS provides intelligent presentation of educational content appropriate for students, such as the degree of knowledge, the desired level of detail, assessment, student level, and familiarity with the subject. Our Intelligent tutoring system was developed using ITSB authoring tool for building ITS. A preliminary evaluation of the ITS was done by a group of students and teachers. The results were acceptable

Automatic assessment of text-based responses in post-secondary education: A systematic review

Text-based open-ended questions in academic formative and summative assessments help students become deep learners and prepare them to understand concepts for a subsequent conceptual assessment. However, grading text-based questions, especially in large courses, is tedious and time-consuming for instructors. Text processing models continue progressing with the rapid development of Artificial Intelligence (AI) tools and Natural Language Processing (NLP) algorithms. Especially after breakthroughs in Large Language Models (LLM), there is immense potential to automate rapid assessment and feedback of text-based responses in education. This systematic review adopts a scientific and reproducible literature search strategy based on the PRISMA process using explicit inclusion and exclusion criteria to study text-based automatic assessment systems in post-secondary education, screening 838 papers and synthesizing 93 studies. To understand how text-based automatic assessment systems have been developed and applied in education in recent years, three research questions are considered. All included studies are summarized and categorized according to a proposed comprehensive framework, including the input and output of the system, research motivation, and research outcomes, aiming to answer the research questions accordingly. Additionally, the typical studies of automated assessment systems, research methods, and application domains in these studies are investigated and summarized. This systematic review provides an overview of recent educational applications of text-based assessment systems for understanding the latest AI/NLP developments assisting in text-based assessments in higher education. Findings will particularly benefit researchers and educators incorporating LLMs such as ChatGPT into their educational activities.Comment: 27 pages, 4 figures, 6 table