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Adaptive online learning can be realized through the evaluation of the learning process. Monitoring and supervising learners' cognitive levels and adjusting learning strategies can increasingly improve the quality of online learning. This analysis is made possible by real-time measurement of learners' cognitive levels during the online learning process. However, most of the currently used techniques for evaluating cognitive levels rely on labour-intensive and time-consuming manual coding. In this study, we explore the machine learning (ML) algorithms and taxonomy of Bloom's cognitive levels to explore features that affect learner's cognitive level in online assessments and the ability to automatically predict learner's cognitive level and thus, come up with a recommendation or pedagogical intervention to improve learner's acquisition. The analysis of 15,182 learners' assessments of a specific learning concept affirms the effectiveness of our approach. We attain an accuracy of 82.21% using ML algorithms. These results are very encouraging and have implications for how automated cognitive-level analysis tools for online learning will be developed in the future.

The creative programming language Processing can be used as a generative architectural design tool, which allows the designer to write design instructions (algorithms) and compute them, obtaining graphical outputs of great interest. This contribution addresses the inclusion of this language in the architecture curriculum, within the context of digital culture and alternative approaches to how digital tools are used and learned. It studies the different processes related to Computational Thinking that are triggered in the prototyping of computer applications and that lead to creativity. The similarity between architectural design and programming is analysed, both in problem solving (abstraction, decomposition, iterative revisions--debugging--, etc.) and in the use of mechanisms of a digital nature (loops, randomness, etc.). The results of the design and testing of a pilot course are shown, in which the way of teaching, learning and using this programming language is based on the graphical representation of problems through sketches.

No records of systematic reviews focused on deep learning in open learning have been found, although there has been some focus on other areas of machine learning. Through a systematic review, this study aimed to determine the trends, applied computational techniques, and areas of educational use of deep learning in open learning. The PRISMA protocol was used, and the Web of Science Core Collection (2019-2023) was searched. VOSviewer was used for networking and clustering, and in-depth analysis was employed to answer the research questions. Among the main results, it is worth noting that the scientific literature has focused on the following areas: (a) predicting student dropout, (b) automatic grading of short answers, and (c) recommending MOOC courses. It was concluded that pedagogical challenges have included the effective personalization of content for different learning styles and the need to address possible inherent biases in the datasets (e.g., socio-demographics, traces, competencies, learning objectives) used for training. Regarding deep learning, we observed an increase in the use of pre-trained models, the development of more efficient architectures, and the growing use of interpretability techniques. Technological challenges related to the use of large datasets, intensive computation, interpretability, knowledge transfer, ethics and bias, security, and cost of implementation were also evident.

Multimodal learning analytics (MMLA) research has shown the feasibility of building automated models of collaboration quality using artificial intelligence (AI) techniques (e.g., supervised machine learning (ML)), thus enabling the development of monitoring and guiding tools for computer-supported collaborative learning (CSCL). However, the practical applicability and performance of these automated models in authentic settings remains largely an under-researched area. In such settings, the quality of data features or attributes is often affected by noise, which is referred to as attribute noise. This paper undertakes a systematic exploration of the impact of attribute noise on the performance of different collaboration-quality estimation models. Moreover, we also perform a comparative analysis of different ML algorithms in terms of their capability of dealing with attribute noise. We employ four ML algorithms that have often been used for collaboration-quality estimation tasks due to their high performance: random forest, naive Bayes, decision tree, and AdaBoost. Our results show that random forest and decision tree outperformed other algorithms for collaboration-quality estimation tasks in the presence of attribute noise. The study contributes to the MMLA (and learning analytics (LA) in general) and CSCL fields by illustrating how attribute noise impacts collaboration-quality model performance and which ML algorithms seem to be more robust to noise and thus more likely to perform well in authentic settings. Our research outcomes offer guidance to fellow researchers and developers of (MM)LA systems employing AI techniques with multimodal data to model collaboration-related constructs in authentic classroom settings.

The current study measures the extent to which students' self-regulated learning tactics and learning outcomes change as the result of a deliberate, data-driven improvement in the learning design of mastery-based online learning modules. In the original design, students were required to attempt the assessment once before being allowed to access the learning material. The improved design gave students the choice to skip the first attempt and access the learning material directly. Student learning tactics were measured using a multi-level clustering and process mining algorithm, and a quasi-experiment design was implemented to remove or reduce differences in extraneous factors, including content being covered, time of implementation, and naturally occurring fluctuations in student learning tactics. The analysis suggests that most students who chose to skip the first attempt were effectively self-regulating their learning and were thus successful in learning from the instructional materials. Students who would have failed the first attempt were much more likely to skip it than those who would have passed the first attempt. The new design also resulted in a small improvement in learning outcome and median learning time. The study demonstrates the creation of a closed loop between learning design and learning analytics: first, using learning analytics to inform improvements to the learning design, then assessing the effectiveness and impact of the improvements.

Educational disparities between traditional and non-traditional student groups in higher distance education can potentially be reduced by alleviating social identity threat and strengthening students' sense of belonging in the academic context. We present a use case of how Learning Analytics and Machine Learning can be applied to develop and implement an algorithm to classify students as at-risk of experiencing social identity threat. These students would be presented with an intervention fostering a sense of belonging. We systematically analyze the intervention's intended positive consequences to reduce structural discrimination and increase educational equity, as well as potential risks based on privacy, data protection, and algorithmic fairness considerations. Finally, we provide recommendations for Higher Education Institutions to mitigate risk of bias and unintended consequences during algorithm development and implementation from an ethical perspective.

Computational thinking (CT) is a concept of growing importance to pre-university education. Yet, CT is often assessed through results, rather than by looking at the CT process itself. Process-based assessments, or assessments that model how a student completed a task, could instead investigate the process of CT as a formative assessment. In this work, we proposed an approach for developing process-based assessments using constructionist tasks specifically for CT assessment in K-12 contexts, with a focus on directly connecting programming artifacts to aspects of CT. We then illustrated such an assessment with 29 students who ranged in CT and programming experience. These students completed both a constructionist task and a traditional CT assessment. Data from the constructionist task was used to build a process-based assessment and results were compared between the two assessment methods. The process-based assessment produced groups of students who differed in their approach to the task with varying levels of success. However, there was no difference between groups of students in the scores on the traditional CT assessment. Process-based assessment from our approach may be useful as formative assessment to give process feedback, localized to the task given to students.

This study was carried out to develop a test to assess algorithmic thinking skills. To this end, the twelve steps suggested by Downing (2006) were adopted. Throughout the test development, 24 middle school sixth-grade students and eight experts in different areas took part as needed in the tasks on the project. The test was given to 252 students attending the sixth grade who were selected through purposeful sampling. The content validity of the test was ensured by means of obtaining expert opinion, whereas the construct validity was ensured by performing an independent sample t-test on the difference between the lower and upper groups. As a result, the algorithmic thinking skills assessment test was finalized with 22 main items and 2 sample items, totalling 24 items. The KR-20 reliability analysis proved a quite reliable test based on the reliability coefficient of 0.83. As mentioned earlier, the independent sample t-test was applied to the difference of lower and upper groups for construct validation of the test. It was seen that the test items are significant in discriminating the students in the lower and upper groups (p<0.01).

The study focuses on the identification of relationships and/or rules between computational thinking (CT) concepts among the undergraduate students of Applied Informatics due to their attitudes towards mathematics. We analyze three CT concepts -- decomposition, pattern recognition, and algorithmic thinking. We assume that students who have a closer relationship with mathematics, a positive attitude towards mathematics, have better developed CT. We conducted the experiment during the 2022/2023 academic year on the Information Coding and Displaying subject. The results indicate that those students who have no relationship to mathematics, negative attitude towards mathematics, have no problem with decomposition and pattern recognition, but without significant algorithmic thinking. On the other hand, students who have a close relationship with mathematics are also able to decompose or recognize patterns, but moreover they have shown algorithmic thinking. The contribution of the study comprises the identification of relationships and/or patterns of computational thinking concepts among students who have a relationship to mathematics, who cannot assess their relationship to mathematics, as well as among students who have no relationship to mathematics. Our results indicate a different occurrence of computational thinking concepts as well as links and/or relationships between them.

This study applies the Apriori algorithm to analyse patterns, syntactic structures, and thematic clusters in Chinese studies data from various genres. This study aims to identify recurring linguistic elements in order to shed light on the dynamic nature of the Chinese language across different contexts and time periods. The Apriori algorithm is used to identify frequent item sets and establish associations between linguistic constructs in large datasets (over 20 years). This study examines the complexity of the Chinese language by analysing co-occurrence patterns, syntactic tendencies, and thematic categorizations. This study examines the evolution of language, regional word choices, and cultural nuances. Thematic clusters and sensory associations establish the relationship between language and culture. The study of Chinese language patterns and cultural implications utilises data to advance computational linguistics and theory. Computational models prioritize cultural and historical context analysis for more comprehensive language processing. Theoretical implications help researchers understand language evolution and culture, while practical implications improve language technology tools. The conclusion provides support for research in computational linguistics, cultural studies, and linguistic theory-based holistic language analysis and application.

Artificial Intelligence (AI) has emerged as a prominent area of investigation in the field of education. Also, perceptions, challenges and threats of AI among university professors show notable variations. This study explores university professors' perspectives regarding AI, including their familiarity with AI, its educational impacts, challenges associated with its implementation, and perceived threats. To achieve this, a survey was administered to 65 university professors from diverse Egyptian institutions, both state and private. Subsequent statistical analyses were conducted to treat the collected data. The outcomes of these analyses reveal that university professors possess varying degrees of familiarity with AI. Despite this, they view AI as a valuable educational tool. The study identifies several challenges hindering AI adoption, encompassing difficulties in comprehending and interpreting AI algorithmic outcomes, the intricate autonomy of AI systems, financial implications of implementation, and concerns regarding data privacy. Additionally, the study identifies apprehensions pertaining to AI''s influence on professors' skills, potential dehumanization of pedagogy, adverse effects on students, and the potential obsolescence of professors. These findings bear implications for the integration of AI in educational contexts, highlighting the necessity for further exploration in this evolving field.

This study explores the transformative potential of Generative AI (GenAI) and ChatBots in educational interaction, communication, and the broader implications of human-GenAI collaboration. By examining the related literature through data mining and analytical methods, the paper identifies three main research themes: the revolutionary role of GenAI-powered ChatBots in educational interactions, their capability to enrich social learning, and their dual role as both support and assistance within educational settings. This research further highlights the impact of human-GenAI interaction in education from social, psychological, and cultural perspectives, focusing on social presence as a fundamental component of the teaching and learning process. It discusses the integration of GenAI and ChatBots into education and considers whether this marks the dawn of an algorithmic renaissance that elevates educational experiences or an apocalypse that threatens the very essence of human learning and interaction.

In learning mathematics in the 21st century and after the COVID-19 pandemic, a multimodal role in the process of drawing conclusions involving mathematical symbols or signs is very much needed. This process is called multimodal semiotic reasoning. This research aims to study the literature on multimodal semiotic reasoning research articles. Until April 2023, there were 32 articles on the keyword multimodal semiotic reasoning with the subject of mathematics in the Scopus, Science Direct, and ERIC databases. Furthermore, it is identified based on a) suitability of criteria; b) the similarity of the articles in the three databases. Finally obtained 24 articles that meet the requirements for further research. A literature review on multimodal semiotic reasoning is explored based on a) kinds of mathematical reasoning involving symbols and signs using multimodal, b) multimodal used in research, c) the most researched mathematical concepts and the mathematization process that occurs, d) the role of multimodal semiotic reasoning in mathematics education. The results showed that a) the most dominant type of mathematical reasoning involving objects, symbols, and signs using multimodal was Algorithmic Reasoning (AR), b) the most multimodal source was verbal answers, c) the concept of geometry through a process of vertical-horizontal mathematization was the most researched, and 4) the biggest role of multimodal semiotic reasoning for the benefit of knowledge of mathematics education. In conclusion, multimodal semiotic reasoning has had a major impact on mathematics education and research on this multimodal semiotic reasoning will continue to thrive in the future. [Note: The publication year (2023) shown in the citation on the PDF is incorrect. The correct publication year is 2024.]

This manuscript reports the results of an investigation of the simultaneous prompting procedure to teach a five-step algorithm for solving three addition basic facts. Four preschool students receiving Tier 1 services in their school's multi-tier system of supports (MTSS) framework participated. A multiple probe across participants single case design was employed, and a visual analysis of the data indicate a functional relationship between the independent and dependent variable. Three of the participants achieved the mastery criterion and maintained the targeted learning outcome for 2-6 weeks. These participants applied the five-step algorithm to three sets of novel addition facts and acquired incidental information consisting of mathematics terminology. The fourth participant voluntarily discontinued her involvement in the study but, prior to her withdrawal, her data were similar to that of the other three participants. Social validity data from the participants' teachers confirmed their satisfaction with the study's procedures and participants' learning outcomes. The results extend those from related research and validate simultaneous prompting's effectiveness in teaching mathematics to students receiving Tier 1 and Tier 3 services in an MTSS framework. Topics for future research are discussed, and include investigating the effectiveness of simultaneous prompting to teach mathematics across students receiving services through all MTSS tiers.

The emergence of artificial intelligence (AI) in academia has prompted various debates on the uses, threats, and limitations of tools that can create text for numerous academic purposes. Critics argue that these advancements may provide opportunities for cheating and plagiarism and even replace the art of writing entirely. To reclaim the creativity and depth that academic writing holds, we propose both an innovative approach to safeguard the creativity and depth of academic writing and a scaffolded way to enhance success in terms of authenticity for the author and, by extension, meaning for the reader. This novel conceptual algorithmic trickle filter model aims to inform successful academic writing and embody the writer's agency--a task too sophisticated for current AI tools. Our model provides a scaffolded decision-making process in a highly personal, flexible, and iterative individual writing development tool applied in a health-conscious way. We position this model as a step towards a pedagogic paradigm shift in reclaiming academic writing that, rather than competing with AI, doubles down on the personal self-evaluative aspects that academic writing offers both author and reader.

Predictive student models are increasingly used in learning environments due to their ability to enhance educational outcomes and support stakeholders in making informed decisions. However, predictive models can be biased and produce unfair outcomes, leading to potential discrimination against certain individuals and harmful long-term implications. This has prompted research on fairness metrics meant to capture and quantify such biases. Nonetheless, current metrics primarily focus on predictive performance comparisons between groups, without considering the behavior of the models or the severity of the biases in the outcomes. To address this gap, we proposed a novel metric in a previous work (Verger et al., 2023) named "Model Absolute Density Distance" (MADD), measuring algorithmic unfairness as the difference of the probability distributions of the model's outcomes. In this paper, we extended our previous work with two major additions. Firstly, we provided theoretical and practical considerations on a hyperparameter of MADD, named "bandwidth," useful for optimal measurement of fairness with this metric. Secondly, we demonstrated how MADD can be used not only to measure unfairness but also to mitigate it through postprocessing of the model's outcomes while preserving its accuracy. We experimented with our approach on the same task of predicting student success in online courses as our previous work, and obtained successful results. To facilitate replication and future usages of MADD in different contexts, we developed an open-source Python package called maddlib (https://pypi.org/project/maddlib/). Altogether, our work contributes to advancing the research on fair student models in education.

The rapid development of education informatization has accumulated a large amount of data for learning analytics, and adopting educational data mining to find new patterns of data, develop new algorithms and models, and apply known predictive models to the teaching system to improve learning is the challenge and vision of the education field in the era of big data. Learning intervention, as a core concept of learning analytics, refers to the purposeful and planned adoption of direct or indirect strategies or behaviors based on tracking learning behaviors and integrating information about learners' characteristics to give learners personalized guidance and assistance in order to help learners break through the status quo of learning difficulties and improve their learning abilities, so as to achieve tailored teaching. In this study, data mining was conducted on the performance records of students on math problems in an online learning system, and a learning intervention suggestion prediction model was constructed on the basis of decision tree algorithm using Python, with a view to understanding the effectiveness, willingness, style, and other characteristics of the learners' online learning through the analysis results, providing personalized guidance to students, and enabling teachers to intervene with at-risk students and successfully complete the teaching goals. It was found that the most significant impact of the learning intervention advice provided to learners was the number of hints they sought during the learning process, and that learners who needed to be "intervened" or "monitored" could be categorized into two groups: independent inefficient and dependent inefficient according to the model. Therefore, teachers or adaptive learning systems should intervene in a timely and appropriate way for different types of learning crisis groups to solve the problems of poor learning performance, insufficient commitment to learning, poor learning habits, low participation in learning, low self-efficacy and other problems of learners in different learning scenarios.

In this paper, we present an extended evaluation of a course recommender system designed to support students who struggle in the first semesters of their studies and are at risk of dropping out. The system, which was developed in earlier work using a student-centered design, is based on the explainable k-nearest neighbor algorithm and recommends a set of courses that have been passed by the majority of successful neighbors, that is, students who graduated from the study program. In terms of the number of recommended courses, we found a discrepancy between the number of courses that struggling students are recommended to take and the actual number of courses they take. This indicates that there may be an alternative path that these students could consider. However, the recommended courses align well with the courses taken by students who successfully graduated. This suggests that even students who are performing well could still benefit from the course recommender system designed for at-risk students. In the present work, we investigate a second type of success--a specific minimum number of courses passed--and compare the results with our first approach from previous work. With the second type, the information about success might be already available after one semester instead of after graduation which allows faster growth of the database and faster response to curricular changes. The evaluation of three different study programs in terms of dropout risk reduction and recommendation quality suggests that course recommendations based on students passing at least three courses in the following semester can be an alternative to guide students on a successful path.

We propose a novel approach to address the issue of college student attrition by developing a hybrid model that combines a structural neural network with a piecewise exponential model. This hybrid model not only shows the potential to robustly identify students who are at high risk of dropout, but also provides insights into which factors are most influential in dropout prediction. To evaluate its effectiveness, we compared the predictive performance of our hybrid model with two other survival analysis models: the piecewise exponential model and a hybrid model combining a fully-connected neural network with a piecewise exponential model. Additionally, we compared it to five other cross-sectional models: Ridge Logistic Regression, Lasso Logistic Regression, CART decision tree, Random Forest, and XGBoost decision tree. Our findings demonstrate that the hybrid model outperforms or performs comparably to the other models when predicting dropout among students at the University of Delaware in Spring 2020, Spring 2021, and Spring 2022. Moreover, by categorizing predictors into three distinct groups--academic, economic, and social-demographic--we discovered that academic predictors play a prominent role in distinguishing between dropout and retained students, while other predictors may indirectly influence predictions by impacting academic variables. Consequently, we recommend implementing an intervention program aimed at identifying at-risk students based on their academic performance and activities, with additional consideration for economic and social-demographic factors in customized intervention plans.

In higher education, academic advising is crucial to students' decision-making. Data-driven models can benefit students in making informed decisions by providing insightful recommendations for completing their degrees. To suggest courses for the upcoming semester, various course recommendation models have been proposed in the literature using different data mining techniques and machine learning algorithms utilizing different data types. One important aspect of the data is that usually, courses taken together in a semester fit well with each other. If there is no correlation between the co-taken courses, students may find it more difficult to handle the workload. Based on this insight, we propose using session-based approaches to recommend a set of well-suited courses for the upcoming semester. We test three session-based course recommendation models, two based on neural networks (CourseBEACON and CourseDREAM) and one on tensor factorization (TF-CoC). Additionally, we propose a post-processing approach to adjust the recommendation scores of any base course recommender to promote related courses. Using metrics capturing different aspects of the recommendation quality, our experimental evaluation shows that session-based methods outperform existing popularity-based, association-based, similarity-based, factorization-based, neural networks-based, and Markov chain-based recommendation approaches. Effective course recommendations can result in improved student advising, which, in turn, can improve student performance, decrease dropout rates, and a more positive overall student experience and satisfaction.

Predicting and improving the academic achievement of university students is a multifactorial problem. Considering the low success rates and high dropout rates, particularly in open education programs characterized by mass enrollment, academic success is an important research area with its causes and consequences. This study aimed to solve a classification problem (successful or unsuccessful), predict students' academic success, and identify those at risk. The primary objective was to predict the academic success status with 26,708 students enrolled in Istanbul University open and distance education programs between 2011 and 2017. Predictions were based demographic data and success grades in Turkish, Atatürk's Principles and History of Revolution, English, and Disaster Culture courses. The study utilized classification models from supervised learning algorithms and was conducted using the SPSS Modeler 18 program. Initially, the data was divided into 70% training and 30% test data. Then, models were constructed by using Random Forest, Tree-AS, C&RT, C5.0, CHAID, QUEST, Naive Bayes, Logistic Regression, NeuralNet, and SVM algorithms. Model performances were compared according to accuracy, sensitivity, specificity, F1 score, positive predictive value, negative predictive value, and Matthews Correlation Coefficient criteria. The C&RT model demonstrated the best performance, achieving the highest specificity value of 0.915.

Hollywood screenwriters worry about Artificial Intelligence (AI) replacements taking over their jobs. Famous museums litigate to protect their art from AI infringement. A major retailer scraps a machine-learning based recruitment program that was biased against women. These are just a few examples of how AI is affecting the world of work, learning, and living. MIS and computer science students are among the professional groups who are embarking into careers with nebulous frontiers obscured by the outcroppings brought on by AI. Computer Science and Information System curriculum task forces have recognized the increasing ethical and professional implications developers' work can have beyond the scope of the programmers' code. In this article, the authors examine the professional, legal, and ethical implications of copyrights and algorithmic bias resulting from development of AI-enhanced applications and offer suggestions for addressing these topics in courses considering changes to the CC2020 and IS2020 Model Curriculum frameworks.

The cyber security education market has grownup exponentially, with a CAGR of 13.9 % as reported by Data Intelo. The report published by the World Economic Forum 2023 indicates a shortfall of 2.27 million cyber security experts in 2021 across different roles and hence manifest that Skill-based cyber security education is the need of the hour. Cybersecurity as a field has evolved as a multi-discipline, multi-stakeholder and multi-role discipline. Therefore, the need to address formal education with an outcome-based philosophy is imperative to address for a wider audience with varied past training in their formal education. With the Internet becoming an essential part of human life, providing security of data passed over the Internet is becoming increasingly crucial. Therefore, the role in the organization which is quite demanding is to have expertise in handling and configuring network security, a subdomain of cyber security as a priority area. The rapid increase in the network attack landscape is constantly demanding monitoring of network attacks as well as a need to promote collaborative R&D and education in the sphere of cyber security due to a shortage of skilled resources. Platform-based education is a potential direction to achieve the objective and address the skill gap required in cyber security. The present research proposes a comprehensive web-based platform that can be used to communicate, collaborate and practice various use cases in the domain of network intrusion detection tools using machine learning algorithms and to evaluate user experience. The proposed platform CySecLearn is a collection of various functionalities and features that ensure experiential learning, will help the learner to develop critical thinking and expertise in network security and promote digital literacy in the domain of cyber security.

In the United States and elsewhere, risk assessment algorithms are being used to help inform criminal justice decision-makers. A common intent is to forecast an offender's "future dangerousness." Such algorithms have been correctly criticized for potential unfairness, and there is an active cottage industry trying to make repairs. In this paper, we use counterfactual reasoning to consider the prospects for improved fairness when members of a disadvantaged class are treated by a risk algorithm as if they are members of an advantaged class. We combine a machine learning classifier trained in a novel manner with an optimal transport adjustment for the relevant joint probability distributions, which together provide a constructive response to claims of bias-in-bias-out. A key distinction is made between fairness claims that are empirically testable and fairness claims that are not. We then use confusion tables and conformal prediction sets to evaluate achieved fairness for estimated risk. Our data are a random sample of 300,000 offenders at their arraignments for a large metropolitan area in the United States during which decisions to release or detain are made. We show that substantial improvement in fairness can be achieved consistently with a Pareto improvement for legally protected classes.

Transformer-based models for transfer learning have the potential to achieve high prediction accuracies on text-based supervised learning tasks with relatively few training data instances. These models are thus likely to benefit social scientists that seek to have as accurate as possible text-based measures, but only have limited resources for annotating training data. To enable social scientists to leverage these potential benefits for their research, this article explains how these methods work, why they might be advantageous, and what their limitations are. Additionally, three Transformer-based models for transfer learning, BERT, RoBERTa, and the Longformer, are compared to conventional machine learning algorithms on three applications. Across all evaluated tasks, textual styles, and training data set sizes, the conventional models are consistently outperformed by transfer learning with Transformers, thereby demonstrating the benefits these models can bring to text-based social science research.

Psychology originally defined parent-child conflict in terms of interpersonal relationships, where parent-child conflict is a process of inconsistent attitudes between parents and children that occurs in a family setting. For this end, we aims to investigate the influence of parental awareness on preschoolers' perception of parent-child conflict and the mediating role of preschoolers' self-esteem. This paper proposes a dynamic parent-child relationship discovery algorithm based on the impact of parent-child cooperation activities on preschool children's development. We applied SPSS and Mplus statistical software for data processing and analysis, and Nvivo 11.0 qualitative software for validation and analysis. The reliability of preschool children's perceived parent-child conflict and sub-dimensions were: 0.901, 0.799, 0.791, 0.811, 0.729; the total scale and the retest reliability of each dimension were: 0.914, 0.837, 0.836, 0.792, 0.711. Validated factor analysis using Mplus: RMSEA = 0.075, TLI = 0.856, CFI = 0.876, SRMR = 0.064.

Selecting undergraduate majors or specializations is a crucial decision for students since it considerably impacts their educational and career paths. Moreover, their decisions should match their academic background, interests, and goals to pursue their passions and discover various career paths with motivation. However, such a decision remains challenging are unfamiliar with the job market, the demand for the required skills, and being in the proper placement in a major is not straightforward. Thus, an automatic recommendation system can be helpful for students to assist and guide them in the right decision. In this context, we developed a machine learning model to predict and recommend suitable specializations for undergraduate students according to the job market and student's academic history. Two hundred twenty-five records of students are considered to establish this work. The proposed approach encompasses four major steps, including data preprocessing to clean, scale, and prepare the data for training to avoid obtaining suboptimal results, accompanied by an oversampling process to equal the samples' distribution to prevent the model from being biased or poorly generalized. Furthermore, we conducted a feature selection step using Sequential Backward Selection (SBS) to extract the relevant features to improve the outcomes and reduce the risk of noise. The selected subset is used to train the model using the AdaBoost classifier. We deployed the Genetic algorithm to optimize the classifier's hyperparameters to maximize results. As a result, the findings of this study exhibit noticeable results compared to existing models, with an accuracy of 98.1%. The proposed model can be reliable in guiding undergraduate students through proper decisions regarding selecting their major.

This article evaluates the predictions of an algorithmic-level distributed associative memory model as it introduces, propagates, and resolves ambiguity, and compares it to the predictions of computational-level parallel parsing models in which ambiguous analyses are accounted separately in discrete distributions. By superposing activation patterns that serve as cues to other activation patterns, the model is able to maintain multiple syntactically complex analyses superposed in a finite working memory, propagate this ambiguity through multiple intervening words, then resolve this ambiguity in a way that produces a measurable predictor that is proportional to the log conditional probability of the disambiguating word given its context, marginalizing over all remaining analyses. The results are indeed consistent in cases of complex structural ambiguity with computational-level parallel parsing models producing this same probability as a predictor, which have been shown reliably to predict human reading times.

The public availability of large language models, such as chatGPT, brings additional possibilities and challenges to education. Education institutions have to identify when large language models are used and when text is generated by the student itself. In this paper, chatGPT usage in students' answers is investigated. The main aim of the research was to build a machine learning model that could be used in the evaluation of students' answers to open-ended questions written in the Lithuanian language. The model should determine whether the answers were originally written students or answered with the help of chatGPT. A new dataset of student answers has been collected in to train machine learning models. The dataset consists of original student answers, chatGPT answers, and paraphrased chatGPT answers. A total of more than 1000 answers have been prepared. 24 combinations of text pre-processing algorithms have been analyzed. In text pre-processing, the main focus was on various tokenization methods, such as the Bag of Words and Ngrams, the stemming algorithm, and the stop words list. For the analyzed dataset, these pre-processing methods were more effective than application of multilanguage BERT for document embedding. Based on the features/properties of the dataset, the following learning algorithms have been investigated: artificial neural networks, decision trees, random forest, gradient boosting trees, k-nearest neighbours, and naive Bayes. The main results show that the highest accuracy of 87% in some cases can be obtained using gradient boosting trees, random forests, and artificial neural network algorithms. The lowest accuracy has been obtained using the k-nearest neighbouring algorithm. Furthermore, the results of experimental research suggest that the usage of chatGPT in student answers can be automatically identified.

Visual storytelling, particularly through dance choreographies as showcased in previous AlgoRythmics performances, has been effective in communicating relatively straightforward algorithms in an engaging and memorable way. Nevertheless, when addressing complex algorithmic concepts, an approach with greater expressiveness and flexibility becomes necessary. Consequently, this study introduces stage performances as an innovative solution, using cinematic representation to successfully convey and communicate these intricate concepts and processes. To evaluate the effectiveness of this approach, a short film was designed, produced, and showcased to a second-semester CS2 university course audience studying programming techniques. Following an opening scene that establishes the context, the subsequent three acts vividly depict ad hoc, greedy, and dynamic programming solutions in response to the posed programming challenge. After the screening, a questionnaire was administered, built on four key constructs of the Technology Acceptance Model, as well as other potential facilitating factors. The study reveals 100% positive perceptions of educational benefits, with the vast majority of students expressing agreement regarding the utility, enjoyment, engagement, creativity, filmic quality, and cognitive benefits of short films. Additionally, a remarkable 96% reported the intent to utilize this approach. Our subsequent Structural Equation Modeling analysis discovered that students whose learning styles were in sync with this approach demonstrated a robust correlation between their perception of the method's value, their enjoyment of the process, and their overall attitude towards this pedagogical method. This study confirms the potential of visual storytelling through short films as an effective tool for delivering programming education. The findings provide valuable insights for computer science educators seeking to engage learners and convey complex information in an attractive and effective way.

This study investigated the impact of problematization-oriented scaffolding and structuring-oriented scaffolding, incorporated within instructional videos, on students' computational thinking and their performance in programming education. We recruited 86 participants from three senior classes at a high school. Each of the three classes was assigned to one of the experimental conditions: the problematization-oriented scaffolding group, the structuring-oriented scaffolding group, and the control group, which received no scaffolding. Pre- and post-tests were conducted to assess students' computational thinking and programming performance during a three-week period. We observed significant improvements in the computational thinking skills of the problematization-oriented scaffolding group, particularly in the algorithmic thinking and cooperativity subdimensions, when compared to the control group. Structuring-oriented scaffoldings in videos also helped enhance students' computational thinking, especially in the algorithmic thinking subdimension. In contrast, students in the control group primarily improved their problem-solving skills, although the difference across the three groups was not significant. Furthermore, this study revealed that the use of learning scaffolds, whether problematization-oriented or structured, significantly contributes to students' learning achievements in comparison to the control group. These findings emphasize the importance of selecting the appropriate scaffolding approach to enhance specific dimensions of computational thinking.

Artificial Intelligence (AI) strives to create intelligent machines with human-like abilities. However, like humans, AI can be prone to implicit biases due to flaws in data or algorithms. These biases may cause discriminatory outcomes and decrease trust in AI. Bias in higher education admission may limit access to opportunities and further social inequalities, often due to implicit biases in data processing and decision-making. Addressing and recognizing implicit biases in AI is essential to create equal access to higher education admission and opportunities for students. To combat AI implicit biases, it is necessary to monitor and assess their performance and train them using unbiased data and algorithms. This ensures that all students have equal access to higher education and the opportunities it provides them. While the recent studies reviewed the algorithmic approaches to reducing bias, this article focuses instead on exploring the current understanding of the impacts of AI implicit bias in higher education and its implications for admissions. Furthermore, it evaluates the interactions between AI technology and education, specifically in mitigating AI implicit bias algorithms that can be leveraged to achieve inclusive and equitable quality education and promote lifelong learning opportunities for all.

Dropout refers to the phenomenon of students leaving school before completing their degree or program of study. Dropout is a major concern for educational institutions, as it affects not only the students themselves but also the institutions' reputation and funding. Dropout can occur for a variety of reasons, including academic, financial, personal, and social factors. Therefore, understanding the factors that contribute to dropout and developing effective strategies to prevent it is a critical challenge for educational institutions. In this study, we propose a hybrid deep learning model based on Long Short-Term Memory and Deep Neural Network algorithms for school dropout prediction. The proposed model was compared with previous works and several other machine learning algorithms, including Deep Neural Network (DNN), K-Nearest Neighbors (KNN), Naive Bayes (NB), Multi-Layer Perceptron (MLP), Decision Trees (DT), Support Vector Machine (SVM), and Random Forest (RF). The results showed that the proposed DNN-LSTM model outperforms the other models in terms of accuracy and efficiency.

This article reports on findings from a social design-based study conducted with an intergenerational group of youth, educators and researchers participating in the Learning to Transform (LiTT) Gaming Lab. We advance the notion of AlgoRitmo Literacies, to highlight the ingenuity of youth and educators as they used a tool called Character AI to author lore emerging within a virtual city called LiTT City. We conceptualize AlgoRitmo--a play on the word algorithm--as part inquiry and reflection (the algo or "something" of AI tools), and part action and future-oriented (ritmo as in movement). Inspired by cosmogonies influenced by Coyolxauhqui, the fragmented Aztec moon goddess, this paper illustrates how young people reconfigure AI artifacts, reshape relationships with AI-governed non-playable characters, and repurpose AI tools to envision alternative futures and identities. In identifying AlgoRitmo Literacies, we provide examples of how ChicanX communities subvert ideologies embedded in AI through creative and ingenious interventions in video games and the construction of cyborg Chicanx subjectivities. This paper offers implications for how educators across content areas can leverage gaming, and AI tools, toward consequential literacy development.

A test battery with two different levels of adaptation is presented: a within-subtest level for the selection of the items in the subtests and a between-subtest level to move from one subtest to the next. The battery runs on a two-level model consisting of a regular response model for each of the subtests extended with a second level for the joint distribution of their abilities. The presentation of the model is followed by an optimized MCMC algorithm to update the posterior distribution of each of its ability parameters, select the items to Bayesian optimality, and adaptively move from one subtest to the next. Thanks to extremely rapid convergence of the Markov chain and simple posterior calculations, the algorithm can be used in real-world applications without any noticeable latency. Finally, an empirical study with a battery of short diagnostic subtests is shown to yield score accuracies close to traditional one-level adaptive testing with subtests of double lengths.

Topic models are mathematical and statistical models used to analyze textual data. The objective of topic models is to gain information about the latent semantic space of a set of related textual data. The semantic space of a set of textual data contains the relationship between documents and words and how they are used. Topic models are becoming more common in educational measurement research as a method for analyzing students' responses to constructed-response items. Two popular topic models are latent semantic analysis (LSA) and latent Dirichlet allocation (LDA). LSA uses linear algebra techniques, whereas LDA uses an assumed statistical model and generative process. In educational measurement, LSA is often used in algorithmic scoring of essays due to its high reliability and agreement with human raters. LDA is often used as a supplemental analysis to gain additional information about students, such as their thinking and reasoning. This article reviews and compares the LSA and LDA topic models. This article also introduces a methodology for comparing the semantic spaces obtained by the two models and uses a simulation study to investigate their similarities.

In real-world situations, multidimensional data may appear on large-scale tests or psychological surveys. The purpose of this study was to investigate the effects of the quantity and magnitude of cross-loadings and model specification on item parameter recovery in multidimensional Item Response Theory (MIRT) models, especially when the model was misspecified as a simple structure, ignoring the quantity and magnitude of cross-loading. A simulation study that replicated this scenario was designed to manipulate the variables that could potentially influence the precision of item parameter estimation in the MIRT models. Item parameters were estimated using marginal maximum likelihood, utilizing the expectation-maximization algorithms. A compensatory two-parameter logistic-MIRT model with two dimensions and dichotomous item-responses was used to simulate and calibrate the data for each combination of conditions across 500 replications. The results of this study indicated that ignoring the quantity and magnitude of cross-loading and model specification resulted in inaccurate and biased item discrimination parameter estimates. As the quantity and magnitude of cross-loading increased, the root mean square of error and bias estimates of item discrimination worsened.

Previous studies have found positive effects of Game-Based Learning for mathematics. While most studies assume that this effect is explained by the presence of flow/immersion during games, this has not yet been established. The aim of the current study is to verify if immersion indeed is associated with mathematical skills improvement when using a Game-Based Learning intervention. This was tested among 59 Greek high school students, using authentic design. After having received a traditional education module, the students were tested and then engaged for four weeks in a desktop-based 3D Virtual Learning Environment where they could play mathematic minigames. They were subsequently re-tested to verify if they showed a significant increase in mathematical skills. The students showed an improvement in their mathematical skills (Cohen's d = 1.26), with significant results for functions, geometry, and thinking skills and methods. On the individual level, about half of the students showed a 10% increase in one of the domains (numbers & calculations, functions, geometry, thinking skills and methods, and algorithms and number theory). Immersion was found to be reflected by engagement and presence, but neither one of these aspects was associated with mathematical achievement after the intervention. It is concluded that Game-Based Learning is an effective approach to increasing mathematical skills, yet the underlying mechanisms are not yet understood. The authors discuss several alternative mechanisms based on the literature that can be verified in future studies.

Students enter the science, technology, engineering, and mathematics (STEM) pipeline in primary school, but leak out of it over time for various reasons. To prevent leaks, it is important to understand the variables that affect attitudes towards STEM learning from an early age. This study sought to examine the predictors of young students' STEM learning attitudes. In the study, 493 primary school students (M[subscript age] = 9.62, SD = 0.72) from a Turkish sample were reached through a survey. We recruited our participants using the convenience sampling technique. Data were collected with the STEM learning attitude scale, the Multidimensional 21st Century Skills Scale, and the Computational Thinking (CT) test. Descriptive and correlational analyses were performed on the data. Then the relationship between variables was tested with a structural equation modeling. The results of the analyses showed that STEM learning attitudes and CT skills of primary school students demonstrated good fit indexes. Also results showed that twenty-first century skills mediated the relationship between STEM learning attitudes and CT skills. The results of the analysis are discussed, and recommendations are presented in terms of strengthening young students' place in the STEM pipeline.

Nowadays, e-learning is a significant learning option, especially in light of the COVID-19 pandemic. However, it is a very challenging task because, in online courses, tutors have no direct interaction with students, which causes most of them to lose interest and ultimately drop out of their studies. In regular classes, teachers can see how each student feels about the subject being covered, so they can adjust the pace or pedagogical approach of the lesson to pique students' interest; which is something we cannot find in e-learning platforms. To solve this issue, sentiment analysis of students' written feedback on the course materials is essential. It enables tutors to monitor student behavior during the course and intervene when bored or confused. Text-based sentiment analysis is challenging because emotions are typically taken from the overall context rather than being communicated explicitly through words like "bored". Therefore, researchers have put forth many models. The robustness of machine learning and deep learning algorithms in identifying sentiments from the text was demonstrated. In this study, we propose a potent model for extracting emotions from the text, BERT-LSTM-CNN (BLC), based on deep learning methods. First, we will be using pre-trained Bidirectional Encoder Representations from Transformers (BERT) as a word embedding approach to get features from the text. Then, we will use Long Short-Term Memory (LSTM) to retrieve semantical relations between words and the general context of a sentence, and Convolutional Neural Network (CNN) to catch complex local features. According to the results, our suggested model performs better than existing machine learning and deep learning models in the literature.

This study examined the developmental process of children's computational thinking using block-based programming tools, specifically algorithmic thinking and debugging skills. With this aim, a group of children (N = 191) from two primary schools were studied for two years beginning from the fourth grade, as they engaged in our block-based programming curriculum in their primary schools. A mixed-methods multiple case study was designed with pre- and posttests, classroom observations and postintervention interviews. The statistical results showed that students' algorithmic thinking and debugging skills significantly increased through our intervention, with girls gaining more on algorithmic thinking. During the students' learning process, we found that they demonstrated behavioral, affective, and cognitive engagement while acquiring these skills in schools. This study presents the key to student engagement contributing to the process of computational thinking development, with implications for the design of future computational learning in primary school.

In cyber security education, hands-on training is a common type of exercise to help raise awareness and competence, and improve students' cybersecurity skills. To be able to measure the impact of the design of the particular courses, the designers need methods that can reveal hidden patterns in trainee behavior. However, the support of the designers in performing such analytic and evaluation tasks is ad-hoc and insufficient. With unsupervised machine learning methods, we designed a tool for clustering the trainee actions that can exhibit their strategies or help pinpoint flaws in the training design. By using a k-means++ algorithm, we explore clusters of trainees that unveil their specific behavior within the training sessions. The final visualization tool consists of views with scatter plots and radar charts. The former provides a two-dimensional correlation of selected trainee actions and displays their clusters. In contrast, the radar chart displays distinct clusters of trainees based on their more specific strategies or approaches when solving tasks. Through iterative training redesign, the tool can help designers identify improper training parameters and improve the quality of the courses accordingly. To evaluate the tool, we performed a qualitative evaluation of its outcomes with cybersecurity experts. The results confirm the usability of the selected methods in discovering significant trainee behavior. Our insights and recommendations can be beneficial for the design of tools for educators, even beyond cyber security.

The aim of this study is to examine how algorithmatizing tasks engage mathematics students in algorithmic thinking. Structured, task-based interviews were conducted with eight Year 12 students as they completed a sequence of algorithmatizing tasks involving maximum flow problems. A deductive-inductive analytical process was used to first classify students' mathematical behavior according to four cognitive skills of algorithmic thinking (decomposition, abstraction, algorithmization, and debugging) and then develop sets of subskills to describe how the students engaged these cognitive skills. The findings show how students used algorithmic thinking to solve maximum flow problems and then made progress towards creating a general algorithm before being introduced to the maximum-flow minimum-cut approach, which guarantees a solution.

The use of a pedagogical approach mediated transfer with the bridging method has been successful in facilitating the transitions from block-based to text-based programming languages. Nevertheless, there is a lack of research addressing the impact of this transfer on programming misconceptions during the transition. The way programming concepts are taught to K-12 learners can later result in misconceptions for adult learners. The main objective was to examine the impact of mediated transfer using the bridging method pedagogical approach on the prevalence of programming misconceptions. We conducted a quasi-experimental study in school settings during informatics (computer science) classes among 163 sixth-grade students. The control group received traditional programming lectures using the text-based programming language, Python. Conversely, the experimental group utilized a mediated transfer pedagogical approach by starting with the block-based programming language MakeCode for micro:bit before transitioning to the text-based Python. Our findings indicate that the experimental group significantly reduced programming misconceptions in fundamental programming concepts: variables, sequencing, selection, and loops - compared to the control group. This suggests that the use of block-based programming language as an initial step in programming education, followed by a structured transition to text-based programming language, can effectively mitigate common misconceptions among K-12 learners.

This paper develops a spreadsheet simulation methodology for teaching simulation and performance analysis of priority queues with multiple servers, without resorting to macros, add-ins, or array formula. The approach is made possible by a "single overtaking" simplifying assumption under which any lower-priority customer may be passed in line by at most one higher-priority customer. By increasing the number of overtaking customers, one at a time, the simulation model is extended to the "multiovertaking" case. These simplifying assumptions make such spreadsheet simulations (of more complex queuing networks) accessible to students, and so the paper includes teaching and learning strategies for the classroom. Performance analysis of single-overtaking versus multiovertaking polices is included.

Addressing cognitive disparities has become a paramount concern in computational thinking (CT) education. The intricate and nuanced relationships between CT and cognitive variations emphasize the needs to accommodate diverse cognitive profiles when fostering CT skills, recognizing that these cognitive functions can manifest as either strengths or limitations in different students. Consequently, understanding the connections between students' cognitive functions and CT skills assumes pivotal importance in the design of personalized instructional strategies for CT. Despite a general consideration of learning variability in CT education, empirical insights exploring the correlation between cognitive skills and CT competencies remain notably scarce. This study endeavors to bridge this research gap by investigating the links between executive functions and CT skills, as well as the associations between their sub-dimensions. The results reveal a statistically significant correlation coefficient of 0.452 between these two domains, underscoring the notable connection between executive functions and CT abilities. Furthermore, the sub-dimensional analysis offers a comprehensive understanding of how specific executive functions uniquely contribute to certain CT skills. In light of these findings, this research offers a promising pathway for the development of tailored CT education programs that can cater to the unique needs of each individual, ultimately facilitating inclusive CT programs and making significant contributions to broaden STEM education and future workforce.

The impact of text length on the estimation of lexical diversity has captured the attention of the scientific community for more than a century. Numerous indices have been proposed, and many studies have been conducted to evaluate them, but the problem remains. This methodological review provides a critical analysis not only of the most commonly used indices in language learning studies, but also of the length problem itself, as well as of the methodology for evaluating the proposed solutions. Analysis of three data sets of texts produced by English language learners revealed that indices that reduce all texts to the same length using a probabilistic or an algorithmic approach solve the length-dependency problem; however, all these indices failed to address the second problem, which is their sensitivity to the parameter that determines the length to which the texts are reduced. The paper concludes with recommendations for optimizing lexical diversity analysis.

Large language models (LLMs) are increasingly adopted in educational contexts to provide personalized support to students and teachers. The unprecedented capacity of LLM-based applications to understand and generate natural language can potentially improve instructional effectiveness and learning outcomes, but the integration of LLMs in education technology has renewed concerns over algorithmic bias, which may exacerbate educational inequalities. Building on prior work that mapped the traditional machine learning life cycle, we provide a framework of the LLM life cycle from the initial development of LLMs to customizing pre-trained models for various applications in educational settings. We explain each step in the LLM life cycle and identify potential sources of bias that may arise in the context of education. We discuss why current measures of bias from traditional machine learning fail to transfer to LLM-generated text (e.g., tutoring conversations) because text encodings are high-dimensional, there can be multiple correct responses, and tailoring responses may be pedagogically desirable rather than unfair. The proposed framework clarifies the complex nature of bias in LLM applications and provides practical guidance for their evaluation to promote educational equity.