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Intelligent systems of essay grading constitute important tools for educational technologies. They can significantly replace the manual scoring efforts and provide instructional feedback as well. These systems typically include two main parts: a feature extractor and an automatic grading model. The latter is generally based on computational and artificially intelligent methods. In this work, we focus on the feature extraction part. More precisely, we focus on argumentation and discourse-related features, which constitute high-level features. We discuss some state-of-the-art systems and analyze how argumentation and discourse analysis are used for extracting features and providing feedback.

The purpose of this study was to evaluate the factors that influence behavioural intention (BI) to use the Online Summary-with Automated Feedback (OSAF) in a MOOCs platform. Task-Technology Fit (TTF) was the main framework used to analyse the match between task requirements and technology characteristics, predictng the utilisation of the technology. The relationships between TTF and BI was moderated by students' performance. This TTF provides an illustration of the extent to which the suitability of technology support for tasks will affect the performance and utilization of technology. There were 9 hypotheses examined in this study. The participants consisted of 151 students at a public university in East Java, Indonesia. In order to analyse the collected data, PLS-SEM (partial least squares -- structural equation modeling) was employed, using SmartPLS 3.0. In this study, several points can be concluded, namely: (1) task characteristics and technology characteristics were not positively and significantly effected by TTF, while students' characteristics had a positive and significant effect on TTF; (2) TTF and utilization which are influenced by social influence, have a positive effect on performance impact. In this case the performance impact is constructed from 3 dimensions, namely: learning performance, personal integrity, self-confidence, except TTF were not postitive and were significantly affected by self-confidence. (3) TTF and performance impact positively influence behavioural intention, except in the dimension of performance impact, personal integrity was not postively and significantly effected by behavioural intention.

Evaluation is an essential component of the learning process when discerning learning situations. Assessing natural language responses, like short answers, takes time and effort. Artificial intelligence and natural language processing advancements have led to more studies on automatically grading short answers. In this review, we systematically analyze short-answer evaluation studies. We present the development of the field in terms of scientific production features, datasets, and automatic evaluation features. The field has developed with pioneering studies in the US. Researchers generally conduct applications with English datasets. There has been a significant increase in research in recent years with large language models that support many different languages. These models have applications that achieve accuracy close to that of human evaluators. In addition, deep learning models do not require traditional approaches' detailed preprocessing and feature engineering processes. The dataset size trend is 1000 and above regarding the number of responses. It was observed that metrics such as accuracy, precision, and F1 score were used in performance determination. It is seen that the majority of the studies focus on scoring or rating. In this context, there needs to be more literature on the context of evaluation system applications that can provide descriptive and formative feedback. In addition, the developed assessment systems must be actively used in learning environments.

This study aimed at examining the performance of generative artificial intelligence to extract argumentation elements from text. Thus, the researchers developed a web-based framework to provide automated assessment and feedback relying on a large language model, ChatGPT. The results produced by ChatGPT were compared to human experts across scientific and non-scientific contexts. The findings revealed marked discrepancies in the performance of AI for extracting argument components, with a significant variance between issues of a scientific nature and those that are not. Higher accuracy was noted in identifying claims, data, and qualifiers, as opposed to rebuttals, backing, and warrants. The study illuminated AI's promise for educational applications but also its shortcomings, such as the increased frequency of erroneous element identification when accuracy was low. This highlights the essential need for more in-depth comparative research on models and the further development of AI to enhance its role in supporting argumentation training.

With the fast development of artificial intelligence and emerging technologies, automatic recognition of students' facial expressions has received increased attention. Facial expressions are a kind of external manifestation of emotional states. It is important for teachers to assess students' emotional states and adjust teaching activities accordingly. However, existing methods for automatic facial expression recognition have the limitations of low accuracy of recognition and poor feature extraction. To address the problem, this study proposed a novel deep learning model called DenseNetX-CBAM to improve facial expression recognition by utilizing a variant of densely connected convolutional networks (DenseNet) to reduce unnecessary parameters and strengthen the reuse of expression features between networks; moreover, convolutional block attention module (CBAM) was integrated to allow the networks to focus on important special regions and important channels when representing features. The proposed model was tested using 217 video clips of 33 students in an online course. The results demonstrated promising effects of the method in improving the accuracy of facial expression recognition, which can help teachers to accurately recognize students' emotions and provide real-time adjustment in online learning environments.

Ensuring equity in education is a goal for sustainable development. Among the factors that hinder equity, socioeconomic status (SES) has the highest impact on learning Mathematics. This paper addresses the issue of equity at the secondary school level by proposing an approach based on adopting automatic formative assessment (AFA). Carefully designed mathematical activities with interactive feedback were experimented with a sample of 299 students of grade 8 for a school year. A control group of 257 students learned the same topics using traditional methodologies. Part of the sample belonged to low SES. The learning achievement was assessed through pre-and post-tests to understand if the adoption of AFA impacted learning and whether the results depended on the students' SES. The results show a positive effect of the experimentation (effect size: 0.42). Moreover, the effect size of the experimentation restricted to the low-SES group is high (0.77). In the treatment group, the results do not depend on SES, while in the control group, they do, suggesting that AFA is an equitable approach while traditional instruction risks perpetuating inequalities.

Purpose: Chatbots and artificial intelligence (AI) have the potential to alleviate some of the challenges faced by humans. Faculties frequently swamped with teaching and research may find it difficult to act in a parental role for students by offering them individualized advice. Hence, the primary purpose of this study is to review the literature on chatbots and AI in light of their role in auto-advising systems. The authors aimed to gain insights into the most pertinent topics and concerns related to robo academic advisor and identify any gaps in the literature that could serve as potential avenues for further research. Design/methodology/approach: The research employs a systematic literature review and bibliometric techniques to find 67 primary papers that have been published between 1984 and 2023. Using the Scopus database, the researchers built a summary of the literature on chatbots and AI in academic advice. Findings: Chatbot applications can be a promising approach to address the challenges of balancing personalized student advising with automation. More empirical research is required, especially on chatbots and other AI-based advising systems, to understand their effectiveness and how they can be integrated into educational settings. Research limitations/implications: This research's sample size may restrict its findings' generalizability. Furthermore, the study's focus on chatbots may overlook the potential benefits of other AI technologies in enhancing robo academic advising systems. Future research could explore the impact of robo academic advisors in diverse societal backgrounds to gain a more comprehensive understanding of their implications. Practical implications: Higher educational institutions (HEIs) should establish a robo academic advising system that serves various stakeholders. The system's chatbots and AI features must be user-friendly, considering the customers' familiarity with robots. Originality/value: This study contributes to a better understanding of HEIs' perceptions of the adoption of chatbots and AI in academic advising by providing insightful information about the main forces behind robo academic advising, illuminating the most frequently studied uses of chatbots and AI in academic advising.

Artificial intelligence (AI) and automation are newly impacting the governance of international students, a temporary resident category significant for both direct economic contributions and the formation of a "pool" of potential future immigrants in many immigrant-dependent countries. This paper focuses on tensions within Canada's education-migration ("edugration") system as new technologies intersect with migration regimes, which in turn relate to broader issues of security, administrative burdens, migration governance, and border imperialism. Using an Accidental Ethnography (AccE) approach drawing from practitioner-based legal research, we discuss three themes: (1) "bots at the gate" and the guise of AI's objectivity; (2) a murky international edu-tech industry; and (3) the administrative burdens of digitalized application systems. We suggest that researchers, particularly in education, can benefit from the insights of immigration practitioners who often become aware of potential trends before those less embedded in the everyday negotiation of migration governance.

Data transfer between isolated clusters is imperative for cybersecurity education, research, and testing. Such techniques facilitate hands-on cybersecurity learning in isolated clusters, allow cybersecurity students to practice with various hacking tools, and develop professional cybersecurity technical skills. Educators often use these remote learning environments for research as well. Researchers and students use these isolated environments to test sophisticated hardware, software, and procedures using full-fledged operating systems, networks, and applications. Virus and malware researchers may wish to release suspected malicious software in a controlled environment to observe their behavior better or gain the information needed to assist their reverse engineering processes. The isolation prevents harm to networked systems. However, there are times when the data is required to move in such quantities or speeds that it makes downloading onto an intermediate device untenable. This study proposes a novel turnstile model, a mechanism for one-way file transfer from one enterprise system to another without allowing data leakage. This system protects data integrity and security by connecting the isolated environment to the external network via a locked-down interconnection. Using medium-security isolated clusters, the researchers successfully developed a unidirectional file transfer system that acts as a one-way "turnstile" for secure file transfer between systems not connected to the internet or other external networks. The Turnstile system (source code available at github.com/monnin/turnstile) provides unidirectional file transfer between two computer systems. The solution enabled data to be transferred from a source system to a destination system without allowing any data to be transferred back in the opposite direction. The researchers found an automated process of transferring external files to isolated clusters optimized the transfer speed of external files to isolated clusters using Linux distributions and commands.

To estimate the distribution of the number of acquaintances of the members of a society, the network scale-up method asks survey respondents about the number of people they know with features for which national statistics are available. While many features have been used for this purpose, first names have been suggested to produce particularly low levels of transmission error and recall bias. For this method to be precise, a set of names needs to be selected for the survey that jointly represents the population in relevant variables such as gender or age. This article provides a solution approach to finding the optimal set of names. This can be applied to any population for which a joint distribution of first names and relevant variables is available. We show that our approach successfully provides sets of names closely mirroring the population distributions for six countries with different name statistics.

Background: Peer assessment has played an important role in large-scale online learning, as it helps promote the effectiveness of learners' online learning. However, with the emergence of numerical grades and textual feedback generated by peers, it is necessary to detect the reliability of the large amount of peer assessment data, and then develop an effective automated grading model to analyse the data and predict learners' learning results. Objectives: The present study aimed to propose an automated grading model with reliability detection. Methods: A total of 109,327 instances of peer assessment from a large-scale teacher online learning program were tested in the experiments. The reliability detection approach included three steps: recurrent convolutional neural networks (RCNN) was used to detect grade consistency, bidirectional encoder representations from transformers (BERT) was used to detect text originality, and long short-term memory (LSTM) was used to detect grade-text consistency. Furthermore, the automated grading was designed with the BERT-RCNN model. Results and Conclusions: The effectiveness of the automated grading model with reliability detection was shown. For reliability detection, RCNN performed best in detecting grade consistency with an accuracy rate of 0.889, BERT performed best in detecting text originality with an improvement of 4.47% compared to the benchmark model, and LSTM performed best with an accuracy rate of 0.883. Moreover, the automated grading model with reliability detection achieved good performance, with an accuracy rate of 0.89. Compared to the absence of reliability detection, it increased by 12.1%. Implications: The results strongly suggest that the automated grading model with reliability detection for large-scale peer assessment is effective, with the following implications: (1) The introduction of reliability detection is necessary to help filter out low reliability data in peer assessment, thus promoting effective automated grading results. (2) This solution could assist assessors in adjusting the exclusion threshold of peer assessment reliability, providing a controllable automated grading tool to reducing manual workload with high quality. (3) This solution could shift educational institutions from labour-intensive grading procedures to a more efficient educational assessment pattern, allowing for more investment in supporting instructors and learners to improve the quality of peer feedback.

While studies on teacher written feedback and automated writing evaluation (AWE) feedback have proliferated in recent decades, little attention has been paid to how AWE-teacher integrated feedback would influence students' engagement and their writing performance in second language (L2) writing. Against this backdrop, a quasi-experimental design was adopted to address this important gap. In our study, an intervention was implemented in two classes of English major sophomores in China, with a treatment group receiving AWE-teacher integrated feedback and a comparison group receiving teacher feedback. Data were collected from multiple sources over a 13-week semester to explore the Chinese EFL learners' engagement with the integrated feedback and their writing performance. Results showed that the students in the treatment group engaged with feedback more profoundly in behavior and cognition than those in the comparison group while both groups demonstrated similar affective engagement. Furthermore, compared with the comparison group, the treatment group improved their writing performance in content, organization, vocabulary, and language use significantly. Important implications are also discussed.

With the rapid advancement of information technologies, automated writing evaluation technologies have developed so fast that they can be applied to writing assessments. However, scanty studies have pooled the effects of automated writing evaluation on writing performance. Through a PRISMA protocol-based meta-analysis, this study concludes that automated writing evaluation technologies can significantly reduce anxiety such as writing anxiety and computer anxiety compared with those without the assistance of automated writing evaluation. Automated writing evaluation can also significantly improve writing motivation and second language (L2) writing skills. Educators and designers should highlight how to combine automated writing evaluation with human rating methods and maximize the improvements in L2 writing skills. In the future, artificial intelligence may be integrated into automated writing evaluation to develop advanced automated writing evaluation to address higher-level issues in writing practice.

Advances in computational language models increasingly enable adaptive support for self-regulated learning (SRL) in digital learning environments (DLEs; eg, via automated feedback). However, the accuracy of those models is a common concern for educational stakeholders (eg, policymakers, researchers, teachers and learners themselves). We compared the accuracy of four Dutch language models (ie, spaCy medium, spaCy large, FastText and ConceptNet NumberBatch) in the context of secondary school students' learning of causal relations from expository texts, scaffolded by causal diagram completion. Since machine learning relies on human-labelled data for the best results, we used a dataset with 10,193 students' causal diagram answers, compiled over a decade of research using a diagram completion intervention to enhance students' monitoring of their text comprehension. The language models were used in combination with four popular machine learning classifiers (ie, logistic regression, random forests, support vector machine and neural networks) to evaluate their performance on automatically scoring students' causal diagrams in terms of the correctness of events and their sequence (ie, the causal structure). Five performance metrics were studied, namely accuracy, precision, recall, F1 and the area under the curve of the receiver operating characteristic (ROC-AUC). The spaCy medium model combined with the neural network classifier achieved the best performance for the correctness of causal events in four of the five metrics, while the ConceptNet NumberBatch model worked best for the correctness of the causal sequence. These evaluation results provide a criterion for model adoption to adaptively support SRL of causal relations in DLEs.

Effective collaboration and teamwork skills are critical in high-risk sectors, as deficiencies in these areas can result in injuries and risk of death. To foster the growth of these vital skills, immersive learning spaces have been created to simulate real-world scenarios, enabling students to safely improve their teamwork abilities. In such learning environments, multiple dialogue segments can occur concurrently as students independently organise themselves to tackle tasks in parallel across diverse spatial locations. This complex situation creates challenges for educators in assessing teamwork and for students in reflecting on their performance, especially considering the importance of effective communication in embodied teamwork. To address this, we propose an automated approach for generating teamwork analytics based on spatial and speech data. We illustrate this approach within a dynamic, immersive healthcare learning environment centred on embodied teamwork. Moreover, we evaluated whether the automated approach can produce transcriptions and epistemic networks of spatially distributed dialogue segments with a quality comparable to those generated manually for research objectives. This paper makes two key contributions: (1) it proposes an approach that integrates automated speech recognition and natural language processing techniques to automate the transcription and coding of team communication and generate analytics; and (2) it provides analyses of the errors in outputs generated by those techniques, offering insights for researchers and practitioners involved in the design of similar systems.

Multimodal learning analytics (MMLA) research has made significant progress in modelling collaboration quality for the purpose of understanding collaboration behaviour and building automated collaboration estimation models. Deploying these automated models in authentic classroom scenarios, however, remains a challenge. This paper presents findings from an evaluation of collaboration quality estimation models. We collected audio, video and log data from two different Estonian schools. These data were used in different combinations to build collaboration estimation models and then assessed across different subjects, different types of activities (collaborative-writing, group-discussion) and different schools. Our results suggest that the automated collaboration model can generalize to the context of different schools but with a 25% degradation in balanced accuracy (from 82% to 57%). Moreover, the results also indicate that multimodality brings more performance improvement in the case of group-discussion-based activities than collaborative-writing-based activities. Further, our results suggest that the video data could be an alternative for understanding collaboration in authentic settings where higher-quality audio data cannot be collected due to contextual factors. The findings have implications for building automated collaboration estimation systems to assist teachers with monitoring their collaborative classrooms.

Background: Perceptual measures such as speech intelligibility are known to be biased, variant and subjective, to which an automatic approach has been seen as a more reliable alternative. On the other hand, automatic approaches tend to lack explainability, an aspect that can prevent the widespread usage of these technologies clinically. Aims: In the present work, we aim to study the relationship between four perceptual parameters and speech intelligibility by automatically modelling the behaviour of six perceptual judges, in the context of head and neck cancer. From this evaluation we want to assess the different levels of relevance of each parameter as well as the different judge profiles that arise, both perceptually and automatically. Methods and Procedures: Based on a passage reading task from the Carcinologic Speech Severity Index (C2SI) corpus, six expert listeners assessed the voice quality, resonance, prosody and phonemic distortions, as well as the speech intelligibility of patients treated for oral or oropharyngeal cancer. A statistical analysis and an ensemble of automatic systems, one per judge, were devised, where speech intelligibility is predicted as a function of the four aforementioned perceptual parameters of voice quality, resonance, prosody and phonemic distortions. Outcomes and Results: The results suggest that we can automatically predict speech intelligibility as a function of the four aforementioned perceptual parameters, achieving a high correlation of 0.775 (Spearman's [rho]). Furthermore, different judge profiles were found perceptually that were successfully modelled automatically. Conclusions and Implications: The four investigated perceptual parameters influence the global rating of speech intelligibility, showing that different judge profiles emerge. The proposed automatic approach displayed a more uniform profile across all judges, displaying a more reliable, unbiased and objective prediction. The system also adds an extra layer of interpretability, since speech intelligibility is regressed as a direct function of the individual prediction of the four perceptual parameters, an improvement over more black box approaches.

Cognitive presence is a core construct of the Community of Inquiry (CoI) framework. It is considered crucial for deep and meaningful online-based learning. CoI-based real-time dashboards visualizing students' cognitive presence may help instructors to monitor and support students' learning progress. Such real-time classifiers are often based on the linguistic analysis of the content of posts made by students. It is unclear whether these classifiers could be improved by considering other learning traces, such as files attached to students' posts. We aimed to develop a German-language cognitive presence classifier that includes linguistic analysis using the Linguistic Inquiry and Word Count (LIWC) tool and other learning traces based on 1,521 manually coded meaningful units from an online-based university course. As learning traces, we included not only the linguistic features from the LIWC tool, but also features such as attaching files to a post, tagging, or using terms from the course glossary. We used the k-nearest neighbor method, a random forest model, and a multilayer perceptron as classifiers. The results showed an accuracy of up to 82% and a Cohen's K of 0.76 for the cognitive presence classifier for German posts. Including learning traces did not improve the predictive ability. In conclusion, we developed an automatic classifier for German-language courses based on a linguistic analysis of students' posts. This classifier is a step toward a teacher dashboard. Our work also provides the first fully CoI-coded German dataset for future research on cognitive presence.

The template-based automated item-generation (TAIG) approach that involves template creation, item generation, item selection, field-testing, and evaluation has more steps than the traditional item development method. Consequentially, there is more margin for error in this process, and any template errors can be cascaded to the generated items. Therefore, it is essential to eliminate the source of errors and ensure the quality of the template so items can be problem-free. The article introduces a process to reduce template errors at the early stage of template development, minimize the impact of template errors on generated items, and increase the survival rates of generated items. The article also discusses a statistical method to establish confidence in the quality of the template by systematically examining the quality of the generated items. The proposed method can reduce the review process for some items generated from a template.

The use of automated feedback (AF) has been increasing in English writing courses for emergency remote education (ERE) due to the escalating COVID-19 pandemic crisis in Vietnam. The ERE English writing courses for in-service bachelor programs demanded an effective, fast, free, and user-friendly AF tool that does not require a login ID, which can help students to develop English writing skills and increase their motivation and self-learning ability. The main objectives of Vietnamese in-service students were to improve their English writing skills and update teaching methodologies that integrate technology. Although many studies emphasized the impact of AF tools in normal teaching contexts, relatively less research was conducted the use of AF tools among in-service students. This study examined the contribution of the Marking Mate AF to the improvement of the English writing skills of in-service learners in ERE writing classes. The participants were 82 in-service students in a Vietnamese public university. The dataset included English writing pre and posttest results collected during the first semester of academic year 2020-2021. The mean scores (standard deviation) of these tests were used to benchmark the writing performance of students. A self-report survey was also conducted to investigate the attitude of students toward Marking Mate AF. The results demonstrated that the posttest score (8.739; SD = 0.8495) was higher than the pretest score (7.439; SD = 0.976). Students expressed favorable opinions toward the usefulness of Marking Mate. Based on the findings, the study discussed the pedagogical implications regarding the incorporation of AF in face-to-face and online classes.

Technological tools that provide automated feedback on classroom teaching afford a unique opportunity for educators to engage in self-reflection and work towards improvement goals, in particular to ensure that their instructional environment is equitable and productive for students. More information is needed about how teachers experience automated professional learning tools, including what they perceive as relevant and impactful for their everyday teaching. This mixed-methods study explored the perceptions and engagement of 21 math teachers who used an AI-based tool that generates information about their discourse practices from classroom recordings. Findings indicate that teachers perceived the tool to have a high utility value, especially those who elected to use it over two school years. These teachers increased their use of talk moves over time, suggesting that they were making intentional changes due to their review and uptake of the personalized feedback. These results from this study speak to promising directions for developing AI-based professional learning tools that can support teacher learning and instructional improvement, particularly tools with robust perceived utility.

Learning engagement is an essential indication to define students' learning pacification in the class, and its automated identification technique is the foundation for exploring how to effectively explain the motive of learning impact modifications and making intelligent teaching choices. Current research have demonstrated that there is a direct link between learning engagement and emotional investment and behavioural investment, and it is appropriate and required to apply artificial intelligence to perform autonomous assessment. Unfortunately, the number of relevant research is limited, and the features of learning engagement in certain contexts have not been thoroughly examined. In this research, we highlight the features of a particular application scenario of learning engagement: the application scenario of learning engagement has to incorporate both the coarse-grained information of human body position and the fine-grained information of facial expressions. On the basis of this analysis, a fine-grained learning participation recognition model that suppresses background clutter information is presented. This model can effectively extract coarse and fine-grained information to improve the recognition of learning participation in real-world teaching situations. Particularly, the CNN-Transformer model suggested in this study employs CNN to extract fine-grained information of facial expressions and Transformer to recover coarse-grained information of human body position. Simultaneously, we gathered and categorised real teaching data based on the features of learning engagement situations and enhanced the data quality via crowdsourcing and expert verification. The experimental findings indicate that the CNN-Transformer model can accurately predict the learning engagement of unknown participants with a 92.9% rate of accuracy. Comparative trials reveal that the model's recognition impact is much greater than that of other sophisticated deep learning approaches. Our research offers a framework for future work on deep learning approaches in learning engagement settings.

Differentiated instruction is much demanded yet quite challenging in face of the growing student diversity in today's K-12 classrooms. One major challenge is the provision of differentiated materials to students. Automated text simplification (ATS) tools fueled by natural language processing may serve as a useful assistant for teachers. However, little is known about teachers' contextualized use of ATS over time. This case study traced two teachers' use of ATS systems over a semester. Drawing upon three semi-structured interviews and teacher-generated materials with ATS, we identified an evolving pattern of teachers' engagement with ATS systems, a progression from a blind reliance on the tool to a more critical and coordinated use of the tool over time. We further revealed that teachers' evolving understanding of DI, positioning of the role of ATS systems and human instructors, and interpretation of DI need in specific teaching situations interplayed to shape their particular ways of engagement. Overall, this study contributes to the understanding of teachers' contextualized use of ATS technology for DI. By revealing the influencing factors, the findings hold significant pedagogical implications to inform the design of ATS tools and the creation of favorable conditions to maximize the potential of ATS tools for DI and language teaching and learning in general.

Computer-supported learning technologies are essential for conducting hands-on cybersecurity training. These technologies create environments that emulate a realistic IT infrastructure for the training. Within the environment, training participants use various software tools to perform offensive or defensive actions. Usage of these tools generates data that can be employed to support learning. This paper investigates innovative methods for leveraging the trainee data to provide automated feedback about the performed actions. We proposed and implemented feedback software with four modules that are based on analyzing command-line data captured during the training. The modules feature progress graphs, conformance analysis, activity timeline, and error analysis. Then, we performed field studies with 58 trainees who completed cybersecurity training, used the feedback modules, and rated them in a survey. Quantitative evaluation of responses from 45 trainees showed that the feedback is valuable and supports the training process, even though some features are not fine-tuned yet. The graph visualizations were perceived as the most understandable and useful. Qualitative evaluation of trainees' comments revealed specific aspects of feedback that can be improved. We publish the software as an open-source component of the KYPO Cyber Range Platform. Moreover, the principles of the automated feedback generalize to different learning contexts, such as operating systems, networking, databases, and other areas of computing. Our results contribute to applied research, the development of learning technologies, and the current teaching practice.

The programme outcomes are broad statements of knowledge, skills, and competencies that the students should be able to demonstrate upon graduation from a programme, while the Educational Taxonomy classifies learning objectives into different domains. The precise mapping of a course outcomes to the programme outcome and the educational taxonomy (Cognitive, Psychomotor and Affective) level is crucial to ensure Constructive Alignment at the fundamental level of a course and to ensure meaningful outcome measurements. Unfortunately, this effort is often subject to bias and human error while the use of information technologies as a mediator in this area remains unexplored. This research paper proposes an automatic learning-based advisory system for engineering curriculum to ensure constructive alignment with programme outcomes and educational taxonomy. We demonstrated the use of natural language processing and machine learning techniques to mitigate human error and bias that is often present in such classification tasks. Textual/semantic embeddings, including Term Frequency-Inverse Document Frequency (TF-IDF), Universal Sentence Encoder (USE), and Word2Vec (W2V), machine learning models (Random Forest, Support Vector Machine, Logistic Regression, and Light Gradient Boosting Machine), and their corresponding techniques for optimizing the training process are extensively investigated. In terms of accuracy, we obtained an encouraging result of 78.83%, and 78.71% for TF-IDF with Random Forest, and USE with Support Vector Machine classifier, respectively. We transformed our work into a web-based solution named the Course Outcomes Diagnostic Tool, embedded in the faculty education web platform, Edu Centre that is ubiquitously adopted by the members in the Faculty of Engineering and Technology, Tunku Abdul Rahman University of Management and Technology. The proposed solution has demonstrated great potential in reducing subjectivity, ambiguity, and human error, thereby improving the constructive alignment at the root level of course design to ensures teaching-learning activities are aligned with regulatory body expectations.

Job automation is a topical issue in a technology-driven labor market. However, greater amounts of human capital (e.g., often measured by education, and information-processing skills, including adult literacy) are linked with job security. A knowledgeable and skilled labor force better resists unemployment and/or rebounds from job disruption brought on by job automation. Therefore, the purpose of this study was to advance understanding of the association between educational attainment and literacy, and job automation risk. Using the 2012/2014/2017 Program for the International Assessment of Adult Competencies (PIAAC) data, survey-weighted linear regression was used to model the risk of job automation as a function of education, and literacy proficiency. Higher educational attainment (college or higher vs. less than high school: b = -18.23, p < 0.05) and greater literacy proficiency (score 0-500 points: b = -0.038, p < 0.05) were associated with a decrease in job automation risk among the U.S. workforce.

RAN (Rapid Automatized Naming) is known to be a robust predictor of reading development in different languages. Much less is known about RAN predictive power in bilingual contexts. This is the first meta-analysis of research with bilingual children, assessing the strength of the RAN-reading relationship both within and across languages. It also explored the moderators that may affect this relationship. The search identified 38 published studies of bilingual children with 47 samples, 313 effect sizes and 5312 participants. Analyses of random-effects models with robust variance estimation revealed weak-to-moderate overall effect sizes of RAN and reading concurrently (r = -0.39) and longitudinally (r = -0.38). Moderator analyses of concurrent correlations revealed that RAN correlated more strongly with reading fluency (r = -0.56) than accuracy (r = -0.38). Alphanumeric RAN tasks (digits r = -0.39, letters r = -0.42) showed stronger associations with reading than non-alphanumeric RAN tasks (objects r = -0.38, colors r = -0.25). RAN-reading correlation was statistically significant both within and across languages. It was somewhat weaker when the two skills were measured in different languages (rL1RAN-L2 reading = -0.34, rL2RAN-L1 reading = -0.36) compared to when they were measured in the same language (rL1 = -0.40, rL2 = -0.44), though those differences failed to reach statistical significance. In addition, the type of bilingualism was found to be a potential moderator of the RAN-reading relationship longitudinally, with its magnitude being the strongest in simultaneous bilinguals. Our results suggest that, as a predictor, RAN taps into general, language-independent processes underlying reading.

The COVID-19 pandemic has forced the rapid adoption of remote teaching modalities including "hyflex" where students attend some class sessions in person and some online. Managing the hyflex course requires faculty to quickly generate several reports and to update these reports rapidly when the authorities adjust the rules, students add/drop, or the number of course sessions is changed. The creation of these reports is tedious and error-prone, so they need to be automated. However, the nature of the task precludes traditional Excel programming approaches. We use Excel's new "dynamic array" capabilities (which are available only in Excel 365) to automate the creation and updating of the reports needed to manage the hyflex course. We show how to program the reports, and we discuss the importance of taking an iterative approach to creating effective, error-free cell formulas. To help the reader acquire genuine access to the dynamic array functions, we provide a practical tutorial on the principles and new concepts of dynamic arrays, explain how they relate to legacy array functions, and present selected dynamic array functions including SEQUENCE, FILTER, and XLOOKUP.

Learning-related attention is one of the most important factors influencing learning. Although technologies have enabled the automatic detection of students' attention levels, previous studies mainly focused on colleges or high schools, lacking further validations in primary school students. More importantly, the detected attention might fail to be learning-related if students did not attend learning tasks in the first place, which is common in real-world learning. This phenomenon poses challenges to the practical application of automatic attention detection, especially at the primary school stage, which is crucial for students to develop learning attitudes/strategies. Inspired by the emerging inter-person perspective in neuroscience, we proposed an inter-brain attention coupling method to detect learning-related attention as an extension to the existing single-person-based method. To test this method, wearable electroencephalogram devices were used to monitor students' attention levels in a primary school classroom. We found that one's inter-brain attention coupling, defined as the degree to which an individual student's attention dynamics match the attention dynamics averaged across other classmates, was positively correlated with academic performance: higher performances are associated with higher coupling to the class-average attention dynamics. Moreover, the attention detection framework based on the inter-person perspective outperforms as an indicator of academic performance compared with the widely-used attention level within an individual. The results provide practical insights by extending the applications of detected attention levels from an inter-person perspective and demonstrating its feasibility in monitoring learning-related attention among primary school students.

In this paper, we put in dialogue the local dimension of a nation-state with the global challenges faced by universities worldwide. We focus on the case of Denmark, a nation that was exceptionally active in implementing international university reforms and where digitalisation is a high priority of the public sector governance. The article seeks to contribute to speculative research and critical studies by presenting a mixed-methods study that explores higher education teachers' views about desirable and realistic future scenarios in Denmark. The study draws on data from a survey applied at a large Danish university, analysed both quantitatively (N = 755) and qualitatively (N = 53). The findings show that teachers share clusters of concern about uncontrolled digitalisation and teaching automation, commodification of education, and modularisation of university courses, the latter being a contested but realistic perspective in the latest political reforms. They reaffirm the mission of the university as preparing students for solving real problems and contributing to the challenges of the present time. The paper concludes with a call for university management to recalibrate future imaginaries to the values expressed by the teachers, and the university they wish to create.

This bibliometric analysis attempts to map out the scientific literature on automated writing evaluation (AWE) systems for teaching, learning, and assessment. A total of 170 documents published between 2002 and 2021 in Social Sciences Citation Index journals were reviewed from four dimensions, namely size (productivity and citations), time (publication growth), space (geographical distribution and publication venues), and composition (topic development). Overall findings show an increasing or expanding trend in all four dimensions, which is likely to continue for the next several years. The field has also shown movement toward methodological and theoretical maturity, especially during the past five years. This study has also provided strong evidence of the positive impact of AWE tools on the different dependent variables, such as writing accuracy, writing quality, and plagiarism. In terms of topic development, data shows that the field has significantly expanded and has maintained a sustained interest in popular research topics. However, a great majority of them remain underexplored. Practical, theoretical, and methodological implications and directions for future studies are discussed.

Video is an increasingly important resource in higher education. A key limitation of lecture video is that it is fundamentally a sequential information stream. Quickly accessing the content aligned with specific learning objectives in a video recording of a classroom lecture is challenging. Recent research has enabled automatic reorganization of a lecture video into segments discussing different subtopics. This paper explores AI generation of visual and textual summaries of lecture video segments to improve navigation. A visual summary consists of a subset of images in the video segment that are considered the most unique and important by image analysis. A textual summary consists of a set of keywords selected from the screen text in the video segment by analyzing several factors including frequency, font size, time on screen, and existence in domain and language dictionaries. Evaluation was performed against keywords and summary images selected by human experts with the following results for the most relevant formulations. AI driven keyword selection yielded an F-1 score of 0.63 versus 0.26 for keywords sampled randomly from valid keyword candidates. AI driven visual summary yielded an F-1 score of 0.70 versus 0.59 for K-medoid clustering that is often employed for similar tasks. Surveys showed that 79% (72%) of the users agreed that a visual (textual) summary made a lecture video more useful. This framework is implemented in Videopoints, a real-world lecture video portal available to educational institutions.

This paper proposes a Project-based team instruction methodology with open-ended projects to teach students critical analysis, design and implementation steps of developing Robotic Process Automation (RPA) for information systems. The use of project-based learning is appropriate for teaching RPA analysis and design with lean Six Sigma tools because of its experimental approach and documentation of logical steps needed to learn how to implement RPA successfully. The approach systematically documents work currently performed and defines future actions of the process while ensuring significant benefits are achieved with the RPA enhanced process. This methodology is important because the application of RPA is not commonly taught in Management Information System (MIS) programs. MIS students may not understand the significance of combined methodology, RPA tool, and usefulness of RPA until they enter the workforce where RPA is rapidly becoming available and easier to implement. The lecture sessions and exercises are valuable because it is easy to communicate the value of RPA in terms of time, quality, volume of transactions, etc. using Lean Six Sigma analytic approaches. The exercises involve hands on activities to make this learning experience interesting for students to readily associate the theoretical process improvement agreement and visualize the practical value of RPA enhanced projects. The paper discusses the need for process changes (and new development approaches) in organization to match the properties and functions within enterprise systems and ERPs that has led to criticism of the enterprise systems. This criticism is attributable to the ERPs' many subfunctions and operations that have limited adaptability and reduced functional and operational flexibility. The RPAs require limited prior knowledge of ERPs or their sub-processes for the improvements that are made in the performance of the organization. Thus, students do not have to "learn" how these enterprise or ERP systems operate to make changes or task improvements. This paper presents a project-based methodology and design approach focusing on development of RPAs that help students learning how to make the improvements using the RPS tools. The students learn that projects can deliver significant and tangible benefits to organizations while engaging students in key activities of the analysis, design and development process from a low code-no code perspective.

The Internet of Things (IoT) is a network of objects that can exchange data with other devices also connected to the Internet. One of the most common consumer examples of IoT is home automation, as a variety of smart devices, including doorbells, lightbulbs, thermostats, and refrigerators are now available which users can control remotely using mobile apps or smart speakers. In this hands-on activity, students will apply their basic skills in accessing wireless networks and using mobile devices to connect an Amazon Echo smart speaker to a home network, configure smart plugs to communicate with the Echo, and develop routines to interact with the smart plugs, smartphones, and other smart devices.

The pandemic has accelerated technological advances in higher education. Learning management systems that make use of the persistent platform, a wide range of communication options, and profoundly immersive qualities quickly become the platform of choice for learning management systems. The blockchain enables non-fungible tokens (NFTs) and uses the blockchain architecture to validate college diplomas and transcripts, opening the way for a diverse variety of e-portfolios owned and shared by students and teachers. Another type of blockchain that is relatively new to the education scene is decentralized autonomous organizations (DAOs). Through this, technology courses, certificates, and more can become automated and authenticated on the blockchain. The current state of the web is rapidly evolving into what is known as Web 3.0. It is the emerging evolution of the internet to make it machine-readable, which includes leveraging technologies such as artificial intelligence (AI), the cloud, and distributed ledger technologies such as blockchain. From the perspectives of the university and the metaverse, this paper discusses the shift in education brought about by NFTs, DAOs, Web 3.0, and the metaverse. The powerful mix of Web 3.0 and the metaverse is about to revolutionize the way people learn and teach in the modern world.

Although automation is not a novelty, high hopes are currently pinned on more and more ingenious devices built with Artificial Intelligence (AI). AI has become a key discussion point in the agendas of governments and multinational agencies, with particular interest in educational applications. This article explores parallels between ideas surrounding AI in education and conceptions proposed in the 17th century by Jan Amos Comenius, known as the father of modern education. Drawing upon illustrations from ongoing research that takes metaphor as its core analytical category, the piece assumes that metaphors are not mere stylistic elements, but strategic persuasive devices. Comenius' "didachography," a portmanteau coined in his 1657 "Didactica Magna" to describe an inclusive educational system, relies heavily on metaphors that suggest remarkable similarities with contemporary EdTech rhetoric, especially on AI-related developments. Whilst exemplifying that ideas and premises entailed in current discourses on EdTech may hark back to centuries-old ideas, the paper argues that, despite taking on varying, contextually situated linguistic expressions, underlying metaphors appear to have endured from Comenius' time to support the advent of an educational system poised to automate teaching and, thus, dispense with a key part of his scheme: the teacher. In closing, the piece suggests that we may need to acknowledge the contingent nature of teaching and learning, perhaps accepting that key aspects of what makes us human may always resist engineering.

Purpose: Artificial intelligence (AI) chatbots, such as ChatGPT and GPT-4, developed by OpenAI, have the potential to revolutionize education. This study explores the potential benefits and challenges of using ChatGPT in education (or "educative AI"). Design/Approach/Methods: This paper proposes a theoretical framework called "IDEE" for educative AI such as using ChatGPT and other generative AI in education, which includes identifying the desired outcomes, determining the appropriate level of automation, ensuring ethical considerations, and evaluating effectiveness. Findings: The benefits of using ChatGPT in education or more generally, educative AI, include a more personalized and efficient learning experience for students as well as easier and faster feedback for teachers. However, challenges such as the untested effectiveness of the technology, limitations in the quality of data, and ethical and safety concerns must also be considered. Originality/Value: This study explored the opportunities and challenges of using ChatGPT in education within the proposed theoretical framework.

Generative AI is here to stay, and we need to explore the potential role of these technologies in distance education and online learning, considering both the benefits and challenges. With many potentials such as customized learning experiences, intelligent tutoring, automated grading, content creation, and personalized career advice, there are also a wide range of challenges such as bias in data and algorithms, lack of transparency, overreliance on AI, data privacy and security, access and equity, automation, and singularity. Based on these considerations, generative AI requires enhancing the scope of current educational roles or adopting new ones, such as facilitators of learning, curators of learning resources, designers of learning experiences, and assessors of learning. Though distance education and online learning are mostly attributed spatial and temporal distances, generative AI should also be considered in terms of transactional distance. While the use of generative AI has the potential to decrease the transactional distance, educators still can play a crucial role in bridging this gap by providing personalized support and guidance throughout the learning process not only in terms of pedagogical aspects, but also from the perspective of pedagogy of care and human-centered learning design.

There is an increasing interest and growing practice in Citizen Science (CS) that goes along with the usage of websites for communication as well as for capturing and processing data and materials. From an educational perspective, it is expected that by integrating information about CS in a formal educational setting, it will inspire teachers to create learning activities. This is an interesting case for using bots to automate the process of data extraction from online CS platforms to better understand its use in educational contexts. Although this information is publicly available, it has to follow GDPR rules. This paper aims to explain (1) how CS communicates and is promoted on websites, (2) how web scraping methods and anonymization techniques have been designed, developed and applied to collect information from online sources and (3) how these data could be used for educational purposes. After the analysis of 72 websites, some of the results obtained show that only 24.8% includes detailed information about the CS project and 48.61% includes information about educational purposes or materials.

The influence of algorithms on society is increasing due to their growing presence in all areas of daily life. Although we are not always aware of it, they sometimes usurp the identity of other social actors. The main purpose of this article is to address the meta-research on the field of artificial intelligence and communication from a holistic perspective that allows us to analyze the state of academic research, as well as the possible effects on these areas and on the democratic system. To this end, we carried out a systematized review of recent literature using quantitative and qualitative approaches. The subject analyzed is changing and novel: it includes the impact and interaction of algorithms, bots, automated processes, and artificial intelligence mechanisms in journalism and communication, as well as their effects on democracy. The results show expanding scientific production, mostly in English, based on theoretical discussion or focused on the perception of communication professionals. The object of study is centered mostly on journalism and democracy, and to a lesser degree on ethics or education. Studies indicate great interest in the effects of the use of algorithms on journalism and democracy, but the answers are still uncertain and the challenges for the coming years are significant.

Although Machine Learning (ML) has already become part of our daily lives, few are familiar with this technology. Thus, in order to help students to understand ML, its potential, and limitations and to empower them to become creators of intelligent solutions, diverse courses for teaching ML in K-12 have emerged. Yet, a question less considered is how to assess the learning of ML. Therefore, we performed a systematic mapping identifying 27 instructional units, which also present a quantitative assessment of the students' learning. The simplest assessments range from quizzes to performance-based assessments assessing the learning of basic ML concepts, approaches, and in some cases ethical issues and the impact of ML on lower cognitive levels. Feedback is mostly limited to the indication of the correctness of the answers and only a few assessments are automated. These results indicate a need for more rigorous and comprehensive research in this area.

Mobile learning has extensively influenced students' learning gains and motivation due to the hardware and software development of mobile devices and applications in recent decades. On-the-go learning increases the capacity for flexible and joyful learning and guarantees access to the instructional content anywhere, anytime. Despite their potential benefits, the presence of mobile applications in students' every educational activity can be detrimental to their cognitive abilities as misuse or overuse of specific applications may influence students' concentration and critical thinking. One such downside is reported for overusing automatic spelling correction software, known as AutoCorrect (AC), in language learning. Uncertainty regarding the educational values of AC has increased as students' spelling skills and vocabulary knowledge have declined. The main problem this study addressed is examining the relationship between EFL learners' AC use and their vocabulary size; and if their academic self-regulation mediates this association. Data were gathered from 101 foreign language learners who completed the measures of AC use, academic self-regulation, and vocabulary size. The results showed significant positive correlations between AC use, vocabulary size, and self-regulation. Further, testing the model supported a mediating role for self-regulation in the relationship between AC use and vocabulary size, suggesting that AC use can lead to more knowledge of English words in the condition of deploying self-regulatory strategies. The study signifies how academic self-regulation can assist learners in exploiting mobile learning (m-learning) to their advantage and attaining their educational goals more efficiently.

This study examined effects of pronunciation training using automatic speech recognition technology on common pronunciation errors of Korean English learners. Participants were divided into two groups. One group was given instruction and training about the use of automatic speech recognition for pronunciation practice. The other group was not given such instruction or training as a control group. A pre- and post-test experimental design was used. The treatment period was four weeks. Participants who were taught about using automatic speech recognition for pronunciation practice showed small but significant improvements in pronunciation accuracy than those who did not. In addition, automatic speech recognition was found to assist in the diagnostic evaluation of common pronunciation errors, although it did not produce statistically significant improvements. Participants responded positively to the use of automatic speech recognition for pronunciation practice and testing, although there remain some concerns over technical aspects of the test.