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Enas Mohammed Hussein Saeed

Bio: Enas Mohammed Hussein Saeed is an academic researcher. The author has contributed to research in topics: Association rule learning & Fuzzy logic. The author has an hindex of 1, co-authored 1 publications receiving 1 citations.

Papers
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Journal ArticleDOI
TL;DR: This paper aims to compare the performance of e-learning using traditional classification and association rule methods before and after combination with fuzzy logic and shows that the use of fuzzy-Apriori algorithm provides a significant increase in accuracy compared to when not using any algorithm.

6 citations


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Journal Article
TL;DR: The Fuzzy Analytic Hierarchy Process (FAHP) and Association Rule Mining methods are combined to rank criteria for evaluating e-Learning systems in order of importance, revealing that “connection quality”, “ease of use” and “visualization” are the top five criteria.
Abstract: Online learning is becoming increasingly popular as a result more courseware is being converted into digital materials, resulting in the rapid development of e-Learning systems. The ways in which users (particular instructors) evaluate e-Learning systems are an important issue. In this study, the Fuzzy Analytic Hierarchy Process (FAHP) and Association Rule Mining methods are combined to rank criteria for evaluating e-Learning systems in order of importance. The proposed evaluation model comprises three steps. In step 1, a hierarchal structure of evaluation criteria is established. In step 2, 30 instructors who have practical experience of e-Learning system are interviewed according to this hierarchal structure. Finally, in step 3, a fuzzy mechanism is utilized to normalize the semantic variation among domain experts. Then, the normalized results of the questionnaires are analyzed to obtain the fuzzy weights (via FAHP) and association rules (via Association Rule Mining) among the evaluation criteria. The results of the analysis reveal that “connection quality”, “ease of use”, “visualization”, “waiting time” and “graphical arrangement of interface” are the top five criteria for evaluating an e-Learning system. A developer of an e-Learning system can improve user experience using these criteria and their priorities accordingly.

6 citations

Journal ArticleDOI
TL;DR: In this paper , the authors present a state-of-the-art review of 11 years of research in student modeling, focusing on learner characteristics, learning indicators, and foundational aspects of dissimilar models.
Abstract: There is a growing interest in developing and implementing adaptive instructional systems to improve, automate, and personalize student education. A necessary part of any such adaptive instructional system is a student model used to predict or analyze learner behavior and inform adaptation. To help inform researchers in this area, this paper presents a state-of-the-art review of 11 years of research (2010-2021) in student modeling, focusing on learner characteristics, learning indicators, and foundational aspects of dissimilar models. We mainly emphasize increased prediction accuracy when using multidimensional learner data to create multimodal models in real-world adaptive instructional systems. In addition, we discuss challenges inherent in real-world multimodal modeling, such as uncontrolled data collection environments leading to noisy data and data sync issues. Finally, we reinforce our findings and conclusions through an industry case study of an adaptive instructional system. In our study, we verify that adding multiple data modalities increases our model prediction accuracy from 53.3% to 69%. At the same time, the challenges encountered with our real-world case study, including uncontrolled data collection environment with inevitably noisy data, calls for synchronization and noise control strategies for data quality and usability.

3 citations

Journal ArticleDOI
TL;DR: A state-of-the-art review of 11 years of research in student modeling, focusing on learner characteristics, learning indicators, and foundational aspects of dissimilar models, shows increased prediction accuracy when using multidimensional learner data to create multimodal models in real-world adaptive instructional systems.
Abstract: There is a growing interest in developing and implementing adaptive instructional systems to improve, automate, and personalize student education. A necessary part of any such adaptive instructional system is a student model used to predict or analyze learner behavior and inform adaptation. To help inform researchers in this area, this paper presents a state-of-the-art review of 11 years of research (2010-2021) in student modeling, focusing on learner characteristics, learning indicators, and foundational aspects of dissimilar models. We mainly emphasize increased prediction accuracy when using multidimensional learner data to create multimodal models in real-world adaptive instructional systems. In addition, we discuss challenges inherent in real-world multimodal modeling, such as uncontrolled data collection environments leading to noisy data and data sync issues. Finally, we reinforce our findings and conclusions through an industry case study of an adaptive instructional system. In our study, we verify that adding multiple data modalities increases our model prediction accuracy from 53.3% to 69%. At the same time, the challenges encountered with our real-world case study, including uncontrolled data collection environment with inevitably noisy data, calls for synchronization and noise control strategies for data quality and usability.

2 citations

Journal ArticleDOI
TL;DR: In this paper , a hybrid approach is used to extract insights and patterns from customer transactions, and then present them in a user-friendly way to human or artificial intelligence decision makers.
Abstract: Abstract In order to increase sales, companies try their best to develop relevant offers that anticipate customer needs. One way to achieve this is by leveraging artificial intelligence algorithms that process data collected based on customer transactions, extract insights and patterns from them, and then present them in a user-friendly way to human or artificial intelligence decision makers. This study is based on a hybrid approach, it starts with an online marketplace dataset that contains many customers’ purchases and ends up with global personalized offers based on three different datasets. The first one, generated by a recommendation system, identifies for each customer a list of products they are most likely to buy. The second is generated with an Apriori algorithm. Apriori is used as an associate rule mining technique to identify and map frequent patterns based on support, confidence, and lift factors, and also to pull important rules between products. The third and last one describes, for each customer, their purchase probability in the next few weeks, based on the BG/NBD model and the average of transactions using the Gamma-Gamma model, as well as the satisfaction based on the CLV and RFMTS models. By combining all three datasets, specific and targeted promotion strategies can be developed. Thus, the company is able to anticipate customer needs and generate the most appropriate offers for them while respecting their budget, with minimum operational costs and a high probability of purchase transformation.
Journal ArticleDOI
TL;DR: In this paper , the effectiveness assessment technique of teaching activities in colleges, universities, and institutions of higher education based on the optimized Apriori algorithm is presented. And the test outcomes confirm that this technique is reliable in evaluating the effectiveness of classroom activities in college and universities, which meaningfully advances the classroom teaching levels and quantitative evaluation abilities.
Abstract: In order to increase the effectiveness and teaching quality of numerous classroom activities in colleges and universities, this paper puts forward the effectiveness assessment technique of teaching activities in colleges, universities, and institutions of higher education based on the optimized Apriori algorithm. A mathematical model for assessing the efficacy and usefulness of classroom activities in colleges and universities is constructed. Teaching contents, teaching attitudes, teaching methods, teaching effects, test results, and students’ performance are introduced as comprehensive evaluation factors, and a decision-making model for assessing the success and efficiency of classroom activities in colleges and universities is established by adopting scientific, reasonable, and systematic teaching methods. Through the grey correlation analysis of classroom teaching quality, the delay characteristic analysis and the adaptive parameter adjustment method are adopted. This paper constructs the optimal Apriori algorithm model of college teaching classroom activities, constructs the optimal evaluation function model of college teaching classroom activities effectiveness evaluation by the method of support-confidence joint estimation, extracts the optimal quality parameter set of college teaching classroom activities by the optimization detection method and association rule mining, and realizes the effectiveness evaluation and multi-dimensional parameter estimation of college teaching classroom activities by the optimized Apriori algorithm. The test outcomes confirm that this technique is reliable in evaluating the effectiveness of classroom activities in colleges and universities, and the directional distribution of association rules of classroom quality in colleges and universities is significant, which meaningfully advances the classroom teaching levels and quantitative evaluation abilities.