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Aim/Purpose: The purpose of this paper is to address the challenges posed by disinformation in an educational context. The paper aims to review existing information assessment techniques, highlight their limitations, and propose a conceptual design for a multimodal, explainable information assessment system for higher education. The ultimate goal is to provide a roadmap for researchers that meets current requirements of information assessment in education. Background: The background of this paper is rooted in the growing concern over disinformation, especially in higher education, where it can impact critical thinking and decision-making. The issue is exacerbated by the rise of AI-based analytics on social media and their use in educational settings. Existing information assessment techniques have limitations, requiring a more comprehensive AI-based approach that considers a wide range of data types and multiple dimensions of disinformation. Methodology: Our approach involves an extensive literature review of current methods for information assessment, along with their limitations. We then establish theoretical foundations and design concepts for EMIAS based on AI techniques and knowledge graph theory. Contribution: We introduce a comprehensive theoretical framework for an AI-based multimodal information assessment system specifically designed for the education sector. It not only provides a novel approach to assessing information credibility but also proposes the use of explainable AI and a three-pronged approach to information evaluation, addressing a critical gap in the current literature. This research also serves as a guide for educational institutions considering the deployment of advanced AI-based systems for information evaluation. Findings: We uncover a critical need for robust information assessment systems in higher education to tackle disinformation. We propose an AI-based EMIAS system designed to evaluate the trustworthiness and quality of content while providing explanatory justifications. We underscore the challenges of integrating this system into educational infrastructures and emphasize its potential benefits, such as improved teaching quality and fostering critical thinking. Recommendations for Practitioners: Implement the proposed EMIAS system to enhance the credibility of information in educational settings and foster critical thinking among students and teachers. Recommendation for Researchers: Explore domain-specific adaptations of EMIAS, research on user feedback mechanisms, and investigate seamless integration techniques within existing academic infrastructure. Impact on Society: This paper's findings could strengthen academic integrity and foster a more informed society by improving the quality of information in education. Future Research: Further research should investigate the practical implementation, effectiveness, and adaptation of EMIAS across various educational contexts.

Background: With increased use of artificial intelligence in the classroom, there is now a need to better understand the complementarity of intelligent learning technology and teachers to produce effective instruction. Objective: The paper reviews the current research on intelligent learning technology designed to make models of student learning and instruction transparent to teachers, an area we call model-based learning analytics. We intended to gain an insight into the coupling between the knowledge models that underpin the intelligent system and the knowledge used by teachers in their classroom decision making. Methods: Using a systematic literature review methodology, we first identified 42 papers, mainly from the domain of intelligent tutoring systems and learning analytics dashboards that conformed to our selection criteria. We then qualitatively analysed the context in which the systems were applied, models they used and benefits reported for teachers and learners. Results and Conclusions: A majority of papers used either domain or learner models, suggesting that instructional decisions are mostly left to teachers. Compared to previous reviews, our set of papers appeared to have a stronger focus on providing teachers with theory-driven insights and instructional decisions. This suggests that model-based learning analytics can address some of the shortcomings of the field, like meaningfulness and actionability of learning analytics tools. However, impact in the classroom still needs further research, as in half of the cases the reported benefits were not backed with evidence. Future research should focus on the dynamic interaction between teachers and technology and how learning analytics has an impact on learning and decision making by teachers and students. We offer a taxonomy of knowledge models that can serve as a starting point for designing such interaction.

The continual evolution of employment opportunities in the present industrial era has raised the need for career-long expert advice. Similar to other fields, thankfully technology has come to our rescue in the area of career guidance also. This paper presents a systematic review of Expert Systems (ES) developed for career guidance, course selection and evaluation of students in the past ten years. The popular research databases Google Scholar and Science Direct were used for obtaining the relevant research papers through broad keywords. The keywords were refined to identify the articles related to rule-based, case-based and fuzzy logic-based ES used for career guidance. A total of twenty-five peer-reviewed relevant articles with full-text available online was selected for the final study. In order to avoid duplicity, technical reports and unreferenced literature were excluded. The review identifies the relatively high weight given by the researchers to rule-based systems owing to their simplicity and broad applicability. However, the relative merits and demerits of rule-based, case-based and fuzzy logic-based ES are highly dependent on the field of application. Nevertheless, ES find wide applications in the area of career guidance and have the potential to enhance the career guidance accessibility of the most remote students.

Background: Artificial intelligence (AI) applications have been implemented across all levels of education, with the rapid developments of chatbots and AI language models, like ChatGPT, demonstrating the urgent need to conceptualize the key debates and their implications for a new era of learning and assessment. This adoption occurs in a context where AI is dramatically remapping "the human," the purposes of schooling, and pedagogy. Focus of Study: The paper examines how different formulations of "human" became interwoven with the sliding signifier of "intelligence" through a series of violent exclusions, and how the shifting contour of "intelligence" produces uneven and unjust ontological scales undergirding both education and AI fields. Its purpose is to engage the education research community in dialogue about biases, the nature of ethics, and decision-making concerning AI in education. Research Design: This paper adapts a historical-philosophical method. It traces the effects of colonialism and racialization within humanism's emergence through Sylvia Wynter's historiography of "figure of Man," especially via the invention of "intelligence," which has linked education and computer science. It also investigates themes central to modern education such as justice, equity, and in/exclusion through a philosophical examination of the ontological scales of "human." Conclusions: After outlining how "intelligence" has shifted from reason-as-morality to concepts of natural intelligence, we argue that current examples of AI in Education (AIEd), like classroom chatbots and social agents, constitute an intermediary point in the arc toward a new computational superintelligence--the emergence of man3--illustrating the opportunities, risks, and ethical issues in pedagogical applications based on emotion. We outline three differing visions of AIEd's future, concluding with a series of provocations (onto-epistemological, practice-based, and purposes of schooling) that exceed such models and that, given rapid innovations in machine learning, require urgent consideration from multiple stakeholders.

Artificial intelligence (AI) and learning analytics (LA) tools are increasingly implemented as decision support for learners and professionals. However, their affordances for guidance purposes have yet to be examined. In this paper, we investigated advantages and challenges of AI-enhanced LA tool for supporting career decisions from the user perspective. Participants (N = 106) interacted with the AI-enhanced LA tool and responded to open-ended questionnaire questions. Content analysis was utilized for the data analysis applying two distinct and robust frameworks: technology acceptance model (TAM) and career decision-making model (CDM) as well as looking into user needs. Results indicate that the AI-enhanced LA tool provided five main benefits to the users: provision of career information, research and analysis of the information, diversification of ideas on possible career paths, providing direction and decision support, and self-reflection. The participants perceived the AI-enhanced LA tool as a supportive asset to be used in transitional life situations characterized with uncertainty. Considerable use difficulties were reported as well as need for further diversification of ideas on possible career paths, need for personalization and self-reflection support, and need for further information. Results regarding perceived support for making career decisions showed that CDM elements were unequally supported by the AI-enhanced LA tool. Most support was focused to investigate smaller number of provided options and make decisions, while contextual information was lacking. Implications for career decision making are discussed.

This paper examines ways in which the ethics of data-driven technologies might be (re)politicised, particularly where educational institutions are involved. The recent proliferation of principles, guidelines, and frameworks for ethical 'AI' (artificial intelligence) have emerged from a plethora of organisations in recent years, and seem poised to impact educational governance. This trend will be firstly shown to align with a narrow form of ethics--deontology--and overlook other potential ways ethical reasoning might contribute to thinking about 'AI'. Secondly, the attention to ethical principles will be suggested to focus excessively on the technology itself, with the effect of masking political concerns for equity and justice. Thirdly and finally, the paper will propose a more radical form of participation in ethical decision-making that not only challenges the assumption of universal consensus, but also draws more authentically on the capacities for debate, contestation, and exchange inherent in the educational institution.

The use of automated decision-making systems is increasing in education. While the potential impacts of ADM are becoming widely known amongst experts, the perspectives of those impacted by ADM remain peripheral. To broaden expertise and participation, this paper proposes that ADM needs to be considered as a sociotechnical controversy, as part of a technical democracy approach that utilises hybrid forums. Following Callon and colleagues, in this paper, technical democracy refers to the process of learning through uncertainty about sociotechnical controversies, and hybrid forums refer to the specific sites of democratisation. This paper first identifies key uses and concerns with ADM in education. Second, it proposes that restricted capacity for participation can be addressed through technical democracy. Last, it proposes that hybrid forums can create moments of democratisation through shared uncertainty, material politics, and collective experimentation.

Collaborative design approaches have been increasingly adopted in the design of learning technologies since they contribute to develop pedagogically inclusive and appropriate learning designs. Despite the positive reception of collaborative design strategies in technology-enhanced learning, little attention has been dedicated to analyzing the challenges faced in design processes using a collaborative approach. In this paper, we disclose the collaborative design of a chatbot for self-regulated learning in higher education using an action research approach. We analyze the design process of EDUguia chatbot, which includes diverse evidence from questionnaires and workshops with students and lecturers, as well as intermediary design objects. Based on the qualitative analysis, we identify several challenges that are transversal to the co-design work, as well as specific to the design phases. We critically reflect on the strategies deployed to overcome these challenges and how they relate to decision-making processes, highlighting the need to make stakeholders' tacit knowledge explicit, cultivate trust-building and support democratic decision-making in technology design processes. We believe that the recommendations we present in this paper contribute to developing best practices in the collaborative design of chatbots for the self-regulation of learning, as well as learning technology in general.

Reviews have long been recognized as among the most important forms of scientific communication. The rapid growth of the primary literature has further increased the need for reviews to distill and interpret the literature. This review on Reviews and Reviewing: Approaches to Research Synthesis encompasses the evolution of the review literature, taxonomy of review literature, uses and users of reviews, the process of preparing reviews, assessment of review quality and impact, the impact of information technology on the preparation of reviews, and research opportunities for information science related to reviews and reviewing. In addition to providing a synthesis of prior research, this review seeks to identify gaps in the published research and to suggest possible future research directions. [ABSTRACT FROM AUTHOR]

We argue in this paper that there is currently no adequate theoretical framework or model that spans the twelve odd year trajectory from non-reader to proficient reader, nor addresses fine-grain skill acquisition, mastery and integration. The target construct itself, reading proficiency, as often operationalized as an endpoint of formal secondary schooling, is defined and measured in imprecise, fragmented terms. Consequently, schools (and empirical research) fall back on heuristics like the Simple View of Reading, or a few stages (learn to read, read to learn, read to do) to describe reading development. Those models, however, are too general to guide instructional decisions for adaptive learning systems. Progress in engineering an adequate learning system has been inhibited by a mismatch with curriculum standards and school organization that impose good-faith but not fully optimized developmental targets on the educational system. We propose the development of a learning and assessment framework to scaffold reading proficiency development while accounting for the diverse learning trajectories of groups or individuals across development. We then identify some key problems, challenges and opportunities that AI technologies are poised to help us address in conceiving individualized, adaptive learning systems for reading proficiency across the developmental spectrum. We close with a selective review of examples of AI-enhanced research or products.

This paper will examine the eight principles of Connectivism Learning Theory and provide examples of how institutions of learning--K-12 and higher education--may think about applying them. Engaging in such work will allow institutions to take advantage of technological platforms as they exist today and into the future. While the Internet has brought with it the opportunity for a connected, collaborative learning landscape, many classrooms and academic institutions today do not take full advantage of the value this connectedness can have for their learners. Using the eight principles as a frame, this paper will offer concrete techniques for K-12 and higher education institutions to engage their learners in the collaborative learning environment of today's technologically connected landscape.

Academic advising is inhibited at most of the high schools to help students identify appropriate academic pathways. The choice of a career domain is significantly influenced by the complexity of life and the volatility of the labor market. Thus, high school students feel confused during the shift period from high school to university, especially with the enormous amounts of data available on the Web. In this paper, an extensive comparative study is conducted to investigate five approaches of recommender systems for university study field and career domain guidance. A novel ontology is constructed to include all the needed information for this purpose. The developed approaches considered user-based and item-based collaborative filtering, demographic-based recommendation, knowledge base supported by case-based reasoning, ontology, as well as different hybridizations of them. A case study on Lebanese high school students is analyzed to evaluate the effectiveness and efficiency of the implemented approaches. The experimental results indicate that the knowledge-based hybrid recommender system, combined with the user-based collaborative filtering and braced with case-based reasoning as well as ontology, generated 98% of similar cases, 95% of them are personalized based on the interests of the high school students. The average usefulness feedback and satisfaction level of the students concerning this proposed hybrid approach reached 95% and 92.5% respectively, which could be a solution to similar problems, regardless of the application domain. Besides, the constructed ontology could be reused in other systems in the educational domain.

This study aims to examine adaptability for educational games in terms of adaptation elements, components used in creating user profiles, and decision algorithms used for adaptation. For this purpose, articles and full-text papers in Web of Science, Google Scholar, and Eric databases between 2000-2021 were searched using the keywords "educational games", "serious games", "game-based learning", "adapt*", "player modeling", "user modeling". After applying the inclusion and exclusion procedures of studies accessed in the search, 26 studies were included in the study. The studies were analyzed in line with the themes determined for the components used in the adaptation of educational games. According to the results, adaptive educational game design was made for a wide variety of fields such as programming teaching, physics, mathematics, computational thinking, and logic. As for adaptive factors; It was determined that adaptations were made for the game, educational content, interface, and non-player character (NPC) behaviors. It is understood that pre-game adaptation and in-game adaptation methods are used as adaptation types. Finally, it is seen that Bayesian networks, artificial neural networks, fuzzy logic, deep learning, item response theory, and decision tree methods are preferred as decision systems in the adaptation process. The findings of this literature review can facilitate the design process by providing a roadmap for researchers interested in adaptive educational game design.

Small and medium-sized enterprises (SMEs) in Europe risk their competitiveness if they fail to embrace digitalization. Indeed, SMEs are aware of the need to digitalize--more than one in two SMEs are concerned that they may lose competitiveness if they do not adopt new digital technologies. However, a key obstacle is related with decision-makers' lack of awareness concerning digital technologies potential and implications. Some decision-makers renounce digital transition simply because they do not understand how it can be incorporated into the business. Take into account this common reality, especially among SMEs, this research project intends to identify the skills and subjects that need to be addressed and suggests the educational methodology and implementation strategy capable of maximizing its success. Therefore, and supported by a focused group research methodology, an innovative training program, oriented to decision-makers, was designed and implemented. The program was conceived based on a self-directed learning methodology, combining both asynchronous lecture/expositive and active training methodologies, strongly based on state-of-the-art knowledge and supported by reference cases and real applications. It is intended that the trainees/participants become familiar with a comprehensive set of concepts, principles, methodologies, and tools, capable of significantly enhancing decision-making capability at both strategic and tactical level. The proposed programme with a multidisciplinary scope explores different thematic chapters (self-contained) as well as cross-cutting thematic disciplines, oriented to the Industry 4.0 and digital transformation paradigm. Topics related with Digital Maturity Assessment, Smart Factories and Flexible Production Systems, Big Data, and Artificial Intelligence for Smarter Decision-Making in Industry and Smart Materials and Products, as well as new production processes for new business models. Each thematic chapter in turn is structured around a variable set of elementary modules and includes examples and case studies to illustrate the selected topics. A teaching-learning methodology centered on an online platform is proposed, having as a central element, a collection of videos complemented by a set of handouts that organize the set of key messages and take-ways associated with each module. In this paper, we present the design and practice of this training course specifically oriented to decision-makers in SME.

Learning analytics dashboards commonly visualize data about students with the aim of helping students and educators understand and make informed decisions about the learning process. To assist with making sense of complex and multidimensional data, many learning analytics systems and dashboards have relied strongly on AI algorithms based on predictive analytics. While predictive models have been successful in many domains, there is an increasing realization of the inadequacies of using predictive models in decision-making tasks that affect individuals without human oversight. In this paper, we employ a suite of state-of-the-art algorithms, from the online analytics processing, data mining, and process mining domains, to present an alternative human-in-the-loop AI method to enable educators to identify, explore, and use appropriate interventions for subpopulations of students with the highest deviation in performance or learning process compared to the rest of the class. We demonstrate an application of our proposed approach in an existing learning analytics dashboard (LAD) and explore the recommended drill-downs in a course with 875 students. The demonstration provides an example of the recommendations from real course data and shows how recommendations can lead the user to interesting insights. Furthermore, we demonstrate how our approach can be employed to develop intelligent LADs.

Purpose: This paper aims to assist higher education institutions (HEIs) in their decision-making process to define initiatives and foster research projects contributing to sustainable development (SD) and minimizing the deficits found in the municipalities. Design/methodology/approach: A documental analysis was performed to select HEIs and Brazilian regional development indicators. Then, the assessment of the sustainable and institutional performance of Brazilian municipalities that have HEIs consisted of three parts: clustering with an unsupervised machine learning model, ranking with a hybrid multi-criteria decision making method and visualization of sustainability performance with the dashboard. Findings: The critical analysis of institutional and sustainability indicators contributes to a more active role of HEIs in matters of social responsibility, with a more holistic view of the performance and quality of municipal education. Furthermore, this critical analysis creates a scenario where HEIs can develop public policy proposals in partnership with the government to mitigate the main issues identified. Social implications: With this study, HEIs will be able to direct their actions to minimize the deficits found in the municipalities, consolidating their social responsibility. Originality/value: This study proposes a new decision-support tool with a dashboard of indicators so that HEIs can foster research projects with a focus on regional SD.

Purpose: This paper aims to demonstrate how skills taxonomies can be used in combination with machine learning to integrate diverse online datasets and reveal skills gaps. The purpose of this study is then to show how the skills gaps revealed by the integrated datasets can be used to achieve better labour market alignment, keep educational offerings up to date and assist graduates to communicate the value of their qualifications. Design/methodology/approach: Using the ESCO taxonomy and natural language processing, this study captures skills data from three types of online data (job ads, course descriptions and resumes), allowing us to compare demand for skills and supply of skills for three different occupations. Findings: This study illustrates three practical applications for the integrated data, showing how they can be used to help workers who are disrupted by technology to identify alternative career pathways, assist educators to identify gaps in their course offerings and support students to communicate the value of their training to employers. Originality/value: This study builds upon existing applications of machine learning (detecting skills from a single dataset) by using the skills taxonomy to integrate three datasets. This study shows how these complementary, big datasets can be integrated to support greater alignment between the needs and offerings of educators, employers and job seekers.

Computational analysis methods and machine learning techniques introduce innovative ways to capture classroom interactions and display data on analytics dashboards. Automated classroom analytics employ advanced data analysis, providing educators with comprehensive insights into student participation, engagement, and behavioral trends within classroom settings. Through the provision of context-sensitive feedback, automated classroom analytics systems can be integrated into the evidence-based pedagogical decision-making and reflective practice processes of faculty members in higher education institutions. This paper presents TEACHActive, an automated classroom analytics system, by detailing its design and implementation. It outlines the processes of stakeholder engagement and mapping, elucidates the benefits and obstacles associated with a comprehensive classroom analytics system design, and concludes by discussing significant implications. These implications propose user-centric design approaches for higher education researchers and practitioners to consider.

This paper introduces the concept of infrastructure into discussions on climate change and education. We focus on the links between the increased use of digital data and the central role of data infrastructures in education, and the energy infrastructure needed to support their growing use in schools and school systems. We elaborate a need for a greater accounting of the climate and related social costs of these interwoven digital and energy infrastructures of schooling. We suggest this is part of the 'disposition' of the infrastructures of schooling that should be weighed into decisions on whether and how to continue with digital technologies in schools. By acknowledging the climate and environmental incommensurability of digital infrastructures, education leaders and young people can more fully understand their dispositions and their costs. We propose three implications for education governance that entail greater consideration of the limits of current school climate change infrastructures such as 'eco school' programs and EdTech 'AI for good' initiatives, pushes for 'computing within limits' without substantial changes, and current school governance practices which unnecessarily rely on digital infrastructures. Instead, what is needed may be a reversal of the extensive use of digital infrastructures by schools and education governance bodies.

We argue in this paper that there is currently no adequate theoretical framework or model that spans the twelve odd year trajectory from non-reader to proficient reader, nor addresses fine-grain skill acquisition, mastery and integration. The target construct itself, reading proficiency, as often operationalized as an endpoint of formal secondary schooling, is defined and measured in imprecise, fragmented terms. Consequently, schools (and empirical research) fall back on heuristics like the Simple View of Reading, or a few stages (learn to read, read to learn, read to do) to describe reading development. Those models, however, are too general to guide instructional decisions for adaptive learning systems. Progress in engineering an adequate learning system has been inhibited by a mismatch with curriculum standards and school organization that impose good-faith but not fully optimized developmental targets on the educational system. We propose the development of a learning and assessment framework to scaffold reading proficiency development while accounting for the diverse learning trajectories of groups or individuals across development. We then identify some key problems, challenges and opportunities that AI technologies are poised to help us address in conceiving individualized, adaptive learning systems for reading proficiency across the developmental spectrum. We close with a selective review of examples of AI-enhanced research or products.

Although an increasing number of ethical data science and AI courses is available, with many focusing specifically on technology and computer ethics, pedagogical approaches employed in these courses rely exclusively on texts rather than on algorithmic development or data analysis. In this paper we recount a recent experience in developing and teaching a technical course focused on responsible data science, which tackles the issues of ethics in AI, legal compliance, data quality, algorithmic fairness and diversity, transparency of data and algorithms, privacy, and data protection. Interpretability of machine-assisted decision-making is an important component of responsible data science that gives a good lens through which to see other responsible data science topics, including privacy and fairness. We provide emerging pedagogical best practices for teaching technical data science and AI courses that focus on interpretability, and tie responsible data science to current learning science and learning analytics research. We focus on a novel methodological notion of the "object-to-interpret-with," a representation that helps students target metacognition involving interpretation and representation. In the context of interpreting machine learning models, we highlight the suitability of "nutritional labels"--a family of interpretability tools that are gaining popularity in responsible data science research and practice.

Purpose: Artificial intelligence (AI) refers to a type of algorithms or computerized systems that resemble human mental processes of decision-making. This position paper looks beyond the sensational hyperbole of AI in teaching and learning. Instead, this paper aims to explore the role of AI in educational leadership. Design/methodology/approach: To explore the role of AI in educational leadership, I synthesized the literature that intersects AI, decision-making, and educational leadership from multiple disciplines such as computer science, educational leadership, administrative science, judgment and decision-making and neuroscience. Grounded in the intellectual interrelationships between AI and educational leadership since the 1950s, this paper starts with conceptualizing decision-making, including both individual decision-making and organizational decision-making, as the foundation of educational leadership. Next, I elaborated on the symbiotic role of human-AI decision-making. Findings: With its efficiency in collecting, processing, analyzing data and providing real-time or near real-time results, AI can bring in analytical efficiency to assist educational leaders in making data-driven, evidence-informed decisions. However, AI-assisted data-driven decision-making may run against value-based moral decision-making. Taken together, both leaders' individual decision-making and organizational decision-making are best handled by using a blend of data-driven, evidence-informed decision-making and value-based moral decision-making. AI can function as an extended brain in making data-driven, evidence-informed decisions. The shortcomings of AI-assisted data-driven decision-making can be overcome by human judgment guided by moral values. Practical implications: The paper concludes with two recommendations for educational leadership practitioners' decision-making and future scholarly inquiry: keeping a watchful eye on biases and minding ethically-compromised decisions. Originality/value: This paper brings together two fields of educational leadership and AI that have been growing up together since the 1950s and mostly growing apart till the late 2010s. To explore the role of AI in educational leadership, this paper starts with the foundation of leadership--decision-making, both leaders' individual decisions and collective organizational decisions. The paper then synthesizes the literature that intersects AI, decision-making and educational leadership from multiple disciplines to delineate the role of AI in educational leadership.

Virtual reality simulation has had a significant impact on training of psychomotor surgical skills, yet there is still a lack of work on its use to teach surgical decision making. This is particularly noteworthy given the recognized importance of decision making in achieving positive surgical outcomes. With the objective of filling this gap, we have developed a system for teaching surgical decision making in the field of endodontics, by integrating a virtual reality simulation environment with a conversational intelligent tutor. This work presents SDMentor (Surgical Decision-making Mentor) -- the first intelligent tutoring system for teaching surgical decision making. In this paper we focus on presenting the intelligent tutoring component of the training system. The design of the system and the teaching approaches are driven by information gained from an observational study of clinical teaching sessions. The tutoring system represents surgical actions and the procedure using a variant of the planning domain definition language (PDDL). Tutorial interaction places emphasis on teaching the rationale for decisions, as well as aspects of situation awareness. We evaluated the quality of the tutorial content by comparing it with tutorial feedback from ten experienced human tutors. We had three expert dental instructors rate the appropriateness of the tutorial feedback given by the human tutors as well as SDMentor over 20 scenarios. Bayesian analysis showed that tutoring interventions of SDMentor were significantly better than interventions by human tutors. To determine whether the expert scores may have been influenced by raters' knowledge of whether interventions came from human tutors or SDMentor, we carried out a type of Turing test. Results show that the expert raters were able to correctly guess which interventions came from SDMentor only 15% of the time, compared to a random baseline accuracy of 9%.

In institutional research, modern data mining approaches are seldom considered to address predictive analytics problems. The goal of this paper is to highlight the advantages of tree-based machine learning algorithms over classic (logistic) regression methods for data-informed decision making in higher education problems, and stress the success of random forest in circumstances where the regression assumptions are often violated in big data applications. Random forest is a model averaging procedure where each tree is constructed based on a bootstrap sample of the data set. In particular, we emphasize the ease of application, low computational cost, high predictive accuracy, flexibility, and interpretability of random forest machinery. Our overall recommendation is that institutional researchers look beyond classical regression and single decision tree analytics tools, and consider random forest as the predominant method for prediction tasks. The proposed points of view are detailed and illustrated through a simulation experiment and analyses of data from real institutional research projects.

Information is everywhere in a hidden and scattered way. It becomes useful when we apply Data mining to extracts the hidden, meaningful, and potentially useful patterns from these vast data resources. Educational data mining ensures a quality education by analyzing educational data based on various aspects. In this paper, we have analyzed the academic results and behavior of some engineering students. For this study, we collect data from 80 students from the CSE department. We gather data from mark sheets and other relevant factors that accelerate the results, collected through a survey. Our main goal is to predict the students' performance. According to this prediction, the counseling department will guide them in advance so that those who are likely to have bad results can do better. The classification can be based on various aspects, as many factors improve the educational system. We have created two datasets focusing on two different angles. Our first dataset classifies and predicts the category of a student (good, bad, medium) on a specific course based on their prerequisite course performance. We have implemented this in the artificial intelligence course. Our second dataset also classifies and predicts the final grade (A, B, C) of any random subject, here we organize our data such a way where it will only focus on how their performance was till the midterm exam. We analyze and compare six classification algorithms. We have focused on all aspects of an algorithm, not only the accuracy level but also the complexity and cost. We have built two final models for two of our datasets based on a decision tree and the naive Bayes algorithms accordingly.

Student success, both during and after college, is central to the mission of higher education. Within the higher-education and, more specifically, the student-success context, the core raison d'être of machine learning (ML) is to help institutions achieve their social mission in an efficient and effective manner. While there should be synergy among people, processes, and ML, this synergy is not often realized because ML algorithms do not yet connect the dots on fully understanding and strategically fostering student success. Transitioning from risk to impact prediction is a catalyst for institutional transformation, which can lead to continuous learning and student-success process innovation. This paper explores how ML can complement and facilitate organizational transformation in promoting a culture of care and innovation through virtuous full-cycle learning.

New cognitive infrastructures are emerging as digital platforms and artificial intelligence enable new forms of automated thinking that shape human decision-making. This paper (a) offers a new theoretical perspective on automated thinking in education policy and (b) illustrates how automated thinking is emerging in one specific policy context. We report on a case study of a policy analysis unit ('The Centre') in an Australian state education department that has been implementing a BI strategy since 2013. The Centre is now focused on using BI to support complex decision making and improve learning outcomes, and their strategy describes this focus as becoming 'information centric'. The theoretical framework for our analysis draws on infrastructure studies and philosophy of technology, particularly Luciana Parisi's recent work on automated thinking. We analyse technical documentation and semi-structured interview data to describe the enactment of a BI strategy in The Centre, with a focus on how new approaches to data analytics are shaping decision-making. Our analysis shows that The Centre is developing a cognitive infrastructure that is already creating new conditions for education policy making, and we conclude with a call for research designs that enable pragmatic exploration of what these infrastructures can do.

Career Guidance is a very critical process that helps individual in making informed decisions about their educational and professional paths. Traditional career guidance systems were dependent on career exploration resources, standardized assessments and human counselors. However, the expanding multiplicity of the workforce and the dynamic nature of the job market, underscored the need for more personalized and advanced career guidance system. The advent of Intelligent Career Guidance System (ICGS), uses Artificial Intelligence (AI) and Machine Learning (ML) which solves this problem. The advance algorithms and data analytics used by the ICGS, helps in the identification of correlations and patterns among the huge datasets of individual profiles and career information. The Intelligent Career Guidance Systems (ICGS) equipped with the power of Artificial Intelligence (AI) and Machine Learning (ML) provides more personalized and customized recommendations lined up with an individual’s unique profile of skills, interests and aptitudes. This thorough review delves into the current condition of ICGS, explaining their various functionalities, components and potential advantages. It also crucially examines the limitations and challenges of ICGS, laying the foundation for future research endeavors. [ABSTRACT FROM AUTHOR]

This paper aims to provide a review of the basis for application of AI in radiology, to discuss the immediate ethical and professional impact in radiology, and to consider possible future evolution. Even if AI does add significant value to image interpretation, there are implications outside the traditional radiology activities of lesion detection and characterisation. In radiomics, AI can foster the analysis of the features and help in the correlation with other omics data. Imaging biobanks would become a necessary infrastructure to organise and share the image data from which AI models can be trained. AI can be used as an optimising tool to assist the technologist and radiologist in choosing a personalised patient's protocol, tracking the patient's dose parameters, providing an estimate of the radiation risks. AI can also aid the reporting workflow and help the linking between words, images, and quantitative data. Finally, AI coupled with CDS can improve the decision process and thereby optimise clinical and radiological workflow. [ABSTRACT FROM AUTHOR]

Mathematics competency is fast becoming an essential requirement in ever greater parts of day-to-day work and life. Thus, creating strategies for improving mathematics learning in students is a major goal of education research. However, doing so requires an ability to look at many aspects of mathematics learning, such as demographics and psychological dispositions, in an integrated way as part of the same system. Large-scale assessments such as the Programme for International Student Assessment (PISA) provide an accessible and large volume of coherent data, and this gives researchers the opportunity to employ data-driven approaches to gain an overview of the system. For these reasons, we have used machine learning to explore the relationships between psychological dispositions and mathematical literacy in Australian 15-year-olds using the PISA 2012 data set. Our results from this strongly data-driven approach re-affirm the primacy of mathematics self-efficacy and highlight novel complex interactions between mathematics self-efficacy, mathematics anxiety and socio-economic status. In this paper, we demonstrate how education researchers can usefully employ data-driven modelling techniques to find complex non-linear relationships and novel interactions in a multidimensional data set.

Virtual Learning Environments have been the center of attention in the last few decades and help educators tremendously with providing students with educational resources. Since artificial intelligence was used for educational proposes, learning management system developers showed much interest in making their products smarter and more intelligent. Nevertheless, the questions of what an intelligent learning management system (ILSM) is and which tools and features are needed to make such system intelligent, are not clearly answered, therefore educational institutes do not have a proper tool to decide upon the degree of intelligence they need for their LMSs. This paper proposes a prevalent, thorough definition of "Intelligent Learning Management Systems", and the design of a fuzzy model to measure the intelligence of these systems. In order to devise a comprehensive definition of an Intelligent Learning Management System, experts from around the world were consulted. Following that, different proposed Intelligent Learning Management Systems were studied, and forty-one features and tools were found and analyzed. After the analysis, experts' opinions were taken into account to rank these features. The paper proposes thirteen most significant features and tools as criteria to be used in fuzzy analytic hierarchy process (AHP) as a fuzzy model to measure the intelligence of Learning Management System.

Contemporary education policy involves the integration of novel forms of data and the creation of new data platforms, in addition to the infusion of business principles into school governance networks, and intensification of socio-technical relations. In this paper, we examine how "computational rationality" may be understood as intensifying of an instrumental set of logics in educational governance and decision making, and/or as opening up new explorations around the uncertainty and incompleteness of policy. We suggest that policy rationalities focused on prediction, transparency and data provide the conditions of possibility for Artificial Intelligence to be integrated into, and intensify aspects of, what we term "computational education policy".

Is it possible to educate a fire officer to deal intelligently with the command and control of a major fire event he will never have experienced? The authors of this paper believe there is, and present here just one solution to this training challenge. It involves the development of an intelligent simulation based upon computer managed interactive media. The expertise and content underpinning this educational development was provided by the West Midlands Fire Service. Their brief for this training programme was unambiguous and to the point: (1) Do not present the trainee with a model answer, because there are no generic fires. Each incident is novel, complex, and often "wicked" in that it changes obstructively as it progresses. Thus firefighting demands that Commanders impose their individual intelligence on each problem to solve it; (2) A suitable Educational Simulator should stand alone; operate in real time; emulate as nearly as possible the "feel" of the fireground; present realistic fire progress; incorporate the vast majority of those resources normally present at a real incident; bombard the trainee with information from those sources; provide as few system-prompts as possible; (3) There should also be an interrogable visual debrief which can be used after the exercise to give the trainees a firm understanding of the effects of their actions. This allows them to draw their own conclusions of their command effectiveness. Additionally, such a record of command and control will be an ideal initiator of tutorial discussion; (4) The simulation should be realisable on a hardware/software platform of 10 000 British Pounds; and (5) The overriding importance is that the simulation should "emulate as nearly as possible the feelings and stresses of the command role". [This paper was first published in "Interactive Learning International," Vol. 8, no. 2, 1992, 109-126, by John Wiley & Sons Ltd. (EJ446122).]

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Application of Artificial Intelligence (AI) in tunnel construction has the potential to transform the industry by improving efficiency, safety, and cost-effectiveness. This paper presents a comprehensive literature review and analysis of hotspots and frontier topics in artificial intelligence-related research in tunnel construction. A total of 554 articles published between 2011 and 2023 were collected from the Web of Science (WOS) core collection database and analyzed using CiteSpace software. The analysis identified three main study areas: Tunnel Boring Machine (TBM) performance, construction optimization, and rock and soil mechanics. The review highlights the advancements made in each area, focusing on design and operation, performance prediction models, and fault detection in TBM performance; computer vision and image processing, neural network algorithms, and optimization and decision-making in construction optimization; and geo-properties and behaviours, tunnel stability and excavation, and risk assessment and safety management in rock and soil mechanics. The paper concludes by discussing future research directions, emphasizing the integration of AI with other advanced technologies, realtime decision-making systems, and the management of environmental impacts in tunnel construction. This comprehensive review provides valuable insights into the current state of AI research in tunnel engineering and serves as a reference for future studies in this rapidly evolving field. [ABSTRACT FROM AUTHOR]

The data of scientific research is important for decision making of sci-tech journals. At present more and more journals attach great importance to their impacts. As the data expands and artificial intelligence develops, it is feasible to help select topic for journals. In the paper we propose a topic selecting framework based on the research focus, which includes analysis of both research focus and paper topics, model training and topic recommendation. Compared with deep neural network, the proposed method could be more accurate with various periods and time intervals. We also predict different models for different kinds of paper. In this way, the model is more adaptable. The experiments demonstrate that our method is 69.8% better than benchmark algorithm in accuracy rate. [ABSTRACT FROM AUTHOR]

In today's world, a significant amount of global energy is used in buildings. Unfortunately, a lot of this energy is wasted, because electrical appliances are not used properly or efficiently. One way to reduce this waste is by detecting, learning, and predicting when people are present in buildings. To do this, buildings need to become "smart" and "cognitive" and use modern technologies to sense when and how people are occupying the buildings. By leveraging this information, buildings can make smart decisions based on recently developed methods. In this paper, we provide a comprehensive overview of recent advancements in Internet of Things (IoT) technologies that have been designed and used for the monitoring of indoor environmental conditions within buildings. Using these technologies is crucial to gathering data about the indoor environment and determining the number and presence of occupants. Furthermore, this paper critically examines both the strengths and limitations of each technology in predicting occupant behavior. In addition, it explores different methods for processing these data and making future occupancy predictions. Moreover, we highlight some challenges, such as determining the optimal number and location of sensors and radars, and provide a detailed explanation and insights into these challenges. Furthermore, the paper explores possible future directions, including the security of occupants' data and the promotion of energy-efficient practices such as localizing occupants and monitoring their activities within a building. With respect to other survey works on similar topics, our work aims to both cover recent sensory approaches and review methods used in the literature for estimating occupancy. [ABSTRACT FROM AUTHOR]

Detecting the student internal state during learning is a key construct in educational environment and particularly in Intelligent Tutoring Systems (ITS). Students' uncertainty is of primary interest as it is deeply rooted in the process of knowledge construction. In this paper we propose a new sensor-based multimodal approach to model users' uncertainty from their affective reactions and cognitive and personal characteristics. An experimental protocol was conducted to record participants' brain activity and physiological signals while they interacted with a computer-based problem solving system and self-reported their perceived level of uncertainty during the tasks. We study key indicators from affective reactions, trait-questionnaire responses, and individual differences that are related to uncertainty states. Then we develop models to automatically predict levels of uncertainty using machine learning techniques. Evidence indicated that students' uncertainty is associated to their mental and emotional reactions. Personal characteristics such as gender, skill level, and personality traits also showed a priori tendencies to be more or less in particular uncertainty states. The SVM algorithm demonstrated the best accuracy results for classifying students' uncertainty levels. Our findings have implications for ITS seeking to continuously monitor users' internal states so they can ultimately provide efficient interventions to enhance learning.