An evolutionary approach for solving the job shop scheduling problem in a service industry

In this paper, an evolutionary-based approach based on the discrete particle swarm optimization (DPSO) algorithm is developed for finding the optimum schedule of a registration problem in a university. Minimizing the makespan, which is the total length of the schedule, in a real-world case study is considered as the target function. Since the selected case study has the characteristics of job shop scheduling problem (JSSP), it is categorized as a NP-hard problem which makes it difficult to be solved by conventional mathematical approaches in relatively short computation time

A solution-based intelligent tutoring system integrated with an online game-based formative assessment: development and evaluation

Nowadays, intelligent tutoring systems are considered an effective research tool for learning systems and problem-solving skill improvement. Nonetheless, such individualized systems may cause students to lose learning motivation when interaction and timely guidance are lacking. In order to address this problem, a solution-based intelligent tutoring system (SITS) is integrated with an online game-based formative assessment game called tic-tac-toe quiz for single-player (TRIS-Q-SP) for learning computer programming. This assessment game combines tic-tac-toe with online assessment, and the rules of tic-tac-toe are revised to stimulate students to use online formative assessment actively. Finally, an experimental study is devised to assess the success of SITS, and significant achievements are observed for the experimental group, besides enjoyment and positive opinions toward the TRIS-Q-SP. Therefore, the practical use of SITS is supported, as the results indicate considerable advantages for the experimental group over the control group. The findings also reveal that immediate elaborated feedback upon answering each question in TRIS-Q-SP is part of an optimal design

Short-term wind speed forecasting by an adaptive network-based fuzzy inference system (ANFIS): an attempt towards an ensemble forecasting method

Accurate Wind speed forecasting has a vital role in efficient utilization of wind farms. Wind forecasting could be performed for long or short time horizons. Given the volatile nature of wind and its dependent on many geographical parameters, it is difficult for traditional methods to provide a reliable forecast of wind speed time series. In this study, an attempt is made to establish an efficient adaptive network-based fuzzy interference (ANFIS) for short-term wind speed forecasting. Using the available data sets in the literature, the ANFIS network is constructed, tested and the results are compared with that of a regular neural network, which has been forecasted the same set of dataset in previous studies. To avoid trial-and-error process for selection of the ANFIS input data, the results of autocorrelation factor (ACF) and partial auto correlation factor (PACF) on the historical wind speed data are employed. The available data set is divided into two parts. 50% for training and 50% for testing and validation. The testing part of data set will be merely used for assessing the performance of the neural network which guarantees that only unseen data is used to evaluate the forecasting performance of the network. On the other hand, validation data could be used for parameter-setting of the network if required. The results indicate that ANFIS could not outperform ANN in short-term wind speed forecasting though its results are competitive. The two methods are hybridized, though simply by weightage, and the hybrid methods shows slight improvement comparing to both ANN and ANFIS results. Therefore, the goal of future studies could be implementing ANFIS and ANNs in a more comprehensive ensemble method which could be ultimately more robust and accurat

Introductory programming: a systematic literature review

As computing becomes a mainstream discipline embedded in the school curriculum and acts as an enabler for an increasing range of academic disciplines in higher education, the literature on introductory programming is growing. Although there have been several reviews that focus on specific aspects of introductory programming, there has been no broad overview of the literature exploring recent trends across the breadth of introductory programming. This paper is the report of an ITiCSE working group that conducted a systematic review in order to gain an overview of the introductory programming literature. Partitioning the literature into papers addressing the student, teaching, the curriculum, and assessment, we explore trends, highlight advances in knowledge over the past 15 years, and indicate possible directions for future research

A flowchart-based intelligent tutoring system model to improve students' problem-solving skills / Danial Hooshyar

Many students fail to succeed in programming courses or face difficulties. Lack of problem-solving skills is one of the most important factors contributing to this challenge. Several researchers believe that forming accurate mental models may yield improvement in novice programmers’ problem-solving skills and should thus be a key goal of any introductory programming course. The flowchart has always been deemed ideal in forming accurate mental models of imperative programming concepts. Another concern is the lack of assistance when students encounter problems, which may lead to demotivation. In order to address this concern, one-to-one tutoring provided by an Intelligent Tutoring System (ITS) is known to be effective. Although numerous ITSs have been developed for the programming field, none are designed to enhance problem-solving skills of novice programmers by focusing less on language and syntax and more on solution designing activities in the shape of flowchart development. Hence, the goal is to address the aforementioned gaps in this thesis by developing and evaluating a novel Flowchart-based Intelligent Tutoring System model (FITS) to produce improvement in students’ problem-solving abilities and help them learn basic and imperative computer programing concepts. The decision-making process in FITS is managed by a Bayesian network to handle uncertainty based on the probability theory. Additionally, an online formative assessment game called Tic-tac-toe Quiz for Single Players (TRIS-Q-SP) is incorporated into FITS to promote student motivation in case timely guidance and interaction are deficient. Unlike other existing ITSs related to computer programming, FITS not only promotes the idea of navigating online learning materials and updating the Bayesian network by applying an online game-based formative assessment, it also offers an adaptive and personalized flowchart development environment. The aim of iv FITS is to improve problem-solving ability besides suggest learning goals along with appropriate reading sequences to students. Therefore, FITS can offer students an accurate mental model of execution, as it visualizes the solution development for a programming problem by converting the given problem statement into a relevant flowchart while actively engaging users in the process. Since a flowchart-based multi-agent system and an online formative assessment game are incorporated into the domain model and student model of the proposed ITS, FITS contributes to two different components of intelligent tutoring systems. FITS also expands and improves on many existing ITSs aimed at teaching programming. At the end of the study, the prototype of FITS was evaluated by university students. According to the results, students who used FITS showed higher scores for the post-test than the pre-test with a learning gain of 60% compared to 36%. A two-tailed paired t-test with a 95% confidence interval was performed against the null hypothesis. The p-value of two-tailed paired t-test of 0.000 was obtained, showing strong evidence against the null hypothesis. Therefore, from the result of this t-test, it can be concluded that the scores in the post-test are significantly higher from the scores in the pre-test and the use of FITS in practice is supported. The students’ opinions about FITS were collected via questionnaires and the results signified that the students really liked FITS, the online game and the personalized flowchart development environment as a learning approach

Predicting Course Grade through Comprehensive Modelling of Students’ Learning Behavioral Pattern

While modelling students’ learning behavior or preferences has been found as a crucial indicator for their course achievement, very few studies have considered it in predicting achievement of students in online courses. This study aims to model students’ online learning behavior and accordingly predict their course achievement. First, feature vectors are developed using their aggregated action logs during a course. Second, some of these feature vectors are quantified into three numeric values that are used to model students’ learning behavior, namely, accessing learning resources (content access), engaging with peers (engagement), and taking assessment tests (assessment). Both students’ feature vectors and behavior model constitute a comprehensive students’ learning behavioral pattern which is later used for prediction of their course achievement. Lastly, using a multiple criteria decision-making method (i.e., TOPSIS), the best classification methods were identified for courses with different sizes. Our findings revealed that the proposed generalizable approach could successfully predict students’ achievement in courses with different numbers of students and features, showing the stability of the approach. Decision Tree and AdaBoost classification methods appeared to outperform other existing methods on different datasets. Moreover, our results provide evidence that it is feasible to predict students’ course achievement with a high accuracy through modelling their learning behavior during online courses

Augmenting Deep Neural Networks with Symbolic Educational Knowledge: Towards Trustworthy and Interpretable AI for Education

Artificial neural networks (ANNs) have proven to be among the most important artificial intelligence (AI) techniques in educational applications, providing adaptive educational services. However, their educational potential is limited in practice due to challenges such as the following: (i) the difficulties in incorporating symbolic educational knowledge (e.g., causal relationships and practitioners’ knowledge) in their development, (ii) a propensity to learn and reflect biases, and (iii) a lack of interpretability. As education is classified as a ‘high-risk’ domain under recent regulatory frameworks like the EU AI Act—highlighting its influence on individual futures and discrimination risks—integrating educational insights into ANNs is essential. This ensures that AI applications adhere to essential educational restrictions and provide interpretable predictions. This research introduces NSAI, a neural-symbolic AI approach that integrates neural networks with knowledge representation and symbolic reasoning. It injects and extracts educational knowledge into and from deep neural networks to model learners’ computational thinking, aiming to enhance personalized learning and develop computational thinking skills. Our findings revealed that the NSAI approach demonstrates better generalizability compared to deep neural networks trained on both original training data and data enriched by SMOTE and autoencoder methods. More importantly, we found that, unlike traditional deep neural networks, which mainly relied on spurious correlations in their predictions, the NSAI approach prioritizes the development of robust representations that accurately capture causal relationships between inputs and outputs. This focus significantly reduces the reinforcement of biases and prevents misleading correlations in the models. Furthermore, our research showed that the NSAI approach enables the extraction of rules from the trained network, facilitating interpretation and reasoning during the path to predictions, as well as refining the initial educational knowledge. These findings imply that neural-symbolic AI not only overcomes the limitations of ANNs in education but also holds broader potential for transforming educational practices and outcomes through trustworthy and interpretable applications

Early Diagnosis of Dementia from Clinical Data by Machine Learning Techniques

Dementia is the most prevalent degenerative disease in seniors in which progression can be prevented or delayed by early diagnosis. In this study, we proposed a two-layer model inspired by the method used in dementia support centers for the early diagnosis of dementia and using machine learning techniques. Data were collected from patients who received dementia screening from 2008 to 2013 at the Gangbuk-Gu center for dementia in the Republic of Korea. The data consisted of the patient’s gender, age, education, the Mini-Mental State Examination in the Korean version of the CERAD Assessment Packet (MMSE-KC) for dementia screening test, and the Korean version of the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD-K) for the dementia precise test. In the proposed model, MMSE-KC data are initially classified into normal and abnormal. In the second stage, CERAD-K data are used to classify dementia and mild cognitive impairment. The performance of each algorithm is compared with that of Naive Bayes, Bayes Network, Begging, Logistic Regression, Random Forest, Support Vector Machine (SVM) and Multilayer Perceptron (MLP) using Precision, Recall and F-measure. Comparing the F-measure values of normal, mild cognitive impairment (MCI), and dementia, the MLP was the highest in the F-measure values of normal with 0.97, while the SVM appear to be the highest in MCI and dementia with 0.739. Using the proposed early diagnosis model for dementia reduces the time and economic burden and can help simplify the diagnosis method for dementia

Bad Smells of Gang of Four Design Patterns: A Decade Systematic Literature Review

Gang of Four (GoF) design patterns are widely approved solutions for recurring software design problems, and their benefits to software quality are extensively studied. However, the occurrence of bad smells in design patterns increases the crisis of degenerating design patterns’ structure and behavior. Their occurrences are detrimental to the benefits of design patterns and they influence software sustainability by increasing maintenance costs and energy consumption. Despite the destructive roles of bad smells in such designs, there are an absence of studies systematically reviewing bad smells of GoF design patterns. This study systematically reviews a 10-year state of the art sample, identifying 16 studies investigating this phenomenon. Following a thorough evaluation of the full contents, we observed that the occurrence of bad smells have been investigated in proportion to four granularity levels of analysis: Design level, category level, pattern level, and role level. We identified 28 bad smells, categorized under code smells and grime symptoms, and emphasized their relationship with GoF pattern types and categories. The utilization of design pattern bad smell detection approaches and datasets were also discussed. Consequently, we observed that the research phenomenon is growing intensively, with a prominent focus of studies analyzing code smell occurrences rather than grime occurrences, at various granularity levels. Finally, we uncovered research gaps and areas with significant potentials for future research

Multi-stage thermal-economical optimization of compact heat exchangers: a new evolutionary-based design approach for real-world problems

The complicated task of design optimization of compact heat exchangers (CHEs) have been effectively performed by using evolutionary algorithms (EAs) in the recent years. However, mainly due to difficulties of handling extra variables, the design approach has been based on constant rates of heat duty in the available literature. In this paper, a new design strategy is presented where variable operating conditions, which better represent real-world problems, are considered. The proposed strategy is illustrated using a case study for design of a plate-fin heat exchanger though it can be employed for all types of heat exchangers without much change. Learning automata based particle swarm optimization (LAPSO), is employed for handling nine design variables while satisfying various equality and inequality constraints. For handling the constraints, a novel feasibility based ranking strategy (FBRS) is introduced. The numerical results indicate that the design based on variable heat duties yields in more cost savings and superior thermodynamics efficiency comparing to a conventional design approach. Furthermore, the proposed algorithm has shown a superior performance in finding the near-optimum solution for this task when it is compared to the most popular evolutionary algorithms in engineering applications, i.e. genetic algorithm (GA) and particle swarm optimization (PSO)