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While gamified technology integration in vocabulary instruction within a flipped classroom has yielded beneficial teaching outcomes, specific studies have raised concerns about potential adverse effects linked to this approach. As a result, conducting a comparative analysis between gamified technology and conventional paper-based methods within the flipped classroom framework has become essential. This analysis aims to foster the development of a targeted teaching approach that adeptly addresses the unique needs of students. This study employed a sequential explanatory research design to examine the effectiveness of flipped classroom with gamified technology and paper-based method in teaching vocabulary to students with different proficiency levels. Quantitative data was gathered from a pretest and a posttest, whilst qualitative data was collected through teachers' guided reflection. Using Academic Word List (300 target words), control groups employed a paper-based, while experimental groups applied gamified technology ("Quizlet," "Kahoot!," "Quizizz," "Socrative," and "Google Form"), which lasted 10 weeks. The participants were 144 non-English major students who took a general English course in the 2nd semester of 2023. Quantitative data analysis ran in SPSS 25 using "Paired Sample t-Test" and "One-way ANOVA." The qualitative data were analyzed using thematic progression. The results showed that gamified technology did not affect students' learning outcomes, while the paper-based method resulted conversely. It revealed that the paper-based method is more effective than gamified technology for students in general, with low proficiency and high-proficiency level. Further, teachers' beliefs admitted distinctive issues that gamified technology was more effective for high-proficiency learners, whereas paper-based was more effective for low-proficiency learners. The difference analysis of quantitative and qualitative data sheds light on discussing threats while implementing gamified technology and possible solutions.

Artificial intelligence tools have presented many challenges and opportunities to transform teaching and learning on college campuses. These changes are significant enough to require colleges to take action to create a framework by which faculty and students can navigate the proper usage of these tools. Rather than working to create entirely new policies strictly for addressing these new technologies, they should instead edit their existing academic integrity and intellectual property statements to incorporate explicit mentions of artificial intelligence.

An objective structured clinical examination (OSCE) has been recognised as a reliable but workload-intensive assessment method across health sciences studies. Though a variety of digital marking tools have been employed to improve marking and feedback provision for OSCEs, many of these require specialist software or maintenance. This pilot study examines the development and trialling of Microsoft Forms as a marking and feedback instrument for an OSCE within a Sport and Exercise Therapy module. This study aims to assess whether the use of a non-specialist digital tool, such as Microsoft Forms, might be able overcome limitations in current assessment procedures and ultimately provide a more effective method for marking and feedback provision for an OSCE. Results from OSCE examiners (N = 8) and students (N = 30) who participated in the pilot indicate that Microsoft Forms does have the potential to provide a more effective experience for examiners and ultimately improve upon feedback provision for students when compared with a paper-based marking tool. However, concerns around the form's ease-of-use may ultimately influence its adoption as a marking instrument above current paper-based methods.

Artificial Intelligence (AI) and related technologies have a long history of being used in education for motivating learners and enhancing learning. However, there have also been critiques for a too uncritical and naïve implementation of AI in education (AIED) and the potential misuse of the technology. With the release of the virtual assistant ChatGPT from OpenAI, many educators and stakeholders were both amazed and horrified by the potential consequences for education. One field with a potential high impact of ChatGPT is programming education in Computer Science (CS), where creating assessments has long been a challenging task due to the vast amount of programming solutions and support on the Internet. This now appears to have been made even more challenging with ChatGPT's ability to produce both complex and seemingly novel solutions to programming questions. With the support of data collected from interactions with ChatGPT during the spring semester of 2023, this position paper investigates the potential opportunities and threats of ChatGPT for programming education, guided by the question: What could the potential consequences of ChatGPT be for programming education? This paper applies a methodological approach inspired by analytic autoethnography to investigate, experiment, and understand a novel technology through personal experiences. Through this approach, the authors have documented their interactions with ChatGPT in field diaries during the spring semester of 2023. Topics for the questions have related to content and assessment in higher education programming courses. A total of 6 field diaries, with 82 interactions (1 interaction = 1 question + 1 answer) and additional reflection notes, have been collected and analysed with thematic analysis. The study finds that there are several opportunities and threats of ChatGPT for programming education. Some are to be expected, such as that the quality of the question and the details provided highly impact the quality of the answer. However, other findings were unexpected, such as that ChatGPT appears to be "lying" in some answers and to an extent passes the Turing test, although the intelligence of ChatGPT should be questioned. The conclusion of the study is that ChatGPT have potential for a significant impact on higher education programming courses, and probably on education in general. The technology seems to facilitate both cheating and enhanced learning. What will it be? Cheating or AI-enhanced learning? This will be decided by our actions now since the technology is already here and expanding fast. [Note: The publication year (2023) shown in the citation on the PDF is incorrect. The correct publication year is 2024.]

This paper reflects on the process of designing and implementing an update to the "Introduction to Information Technology" course at Bentley University, a business university in New England. Driven by a university-wide curriculum reform initiative and following best practices from digital literacy frameworks and IS model curricula, the new CS 100 course, entitled "Solving Business Problems with Information Technology," shifted emphasis from a personal computing context to exploring small business applications of technology. Students networked with university alumni at "Topics in Tech" presentations, where they learned about career options as information technology professionals. The authors describe the pilot courses that led to proposing a new CS 100 course and reflect its challenges and successes. Challenges included meeting the university's goal of transfer friendliness, scaling from a few pilots to multiple sections, consistency of course delivery, and evaluating assessment. Successes include placing course topics in a business context, introducing current technologies, and providing opportunities for students to learn about career opportunities and experiences from university alumni. The paper also describes how CS 100 aligns with the university's student learning goals for the new curriculum. These include developing future-focused skills, encouraging curiosity and critical analysis, communicating and collaborating in different settings, and demonstrating understanding of issues related to ethical use of technology.

Using R to conduct univariate meta-analyses is becoming common for publication. However, R can also conduct multivariate meta-analysis (MMA). However, newcomers to both R and MMA may find using R to conduct MMA daunting. Given that, R may not be easy for those unfamiliar with coding. Likewise, MMA is a topic of advanced statistics. Thus, it may be very challenging for most newcomers to conduct MMA using R. If this holds, this can be viewed as a practice gap. In other words, the practice gap is that researchers are not capable of using R to conduct MMA in practice. This is problematic. This paper alleviates this practice gap by illustrating how to use R (the metaSEM package) to conduct MMA on educational psychology data. Here, the metaSEM package is used to obtain the required MMA text outputs. However, the metaSEM package is not capable of producing the other required graphical outputs. As a result, the metafor package is also used as a complimentary to generate the required graphical outputs. Ultimately, we hope that our audience will be able to apply what they learn from this method paper to conduct MMA using R in their teaching, research, and publication.

High school math projects center on solving real-world examples in a wide variety of situations that require a creative skill set. Creativity in high school mathematics takes place in a subtle form. In contrast, creativity presents itself conspicuously in art. Therefore, this paper will explore the utilization of polynomial equations in drawing various figures as part of algebra class projects. Additionally, these projects incorporate integrated digital tools in mathematics. The paper will analyze three algebraic projects: the first project involves drawing a bird, the second project focuses on drawing a butterfly, and the third project entails drawing a bat using polynomial equations with the technological tool Desmos. All three projects are products of student work that encompass figures requiring up to forty polynomial equations. The students' impressive work demonstrates the power of mathematics in pushing the boundaries of other subjects. As digital technology becomes increasingly integral, mathematical tools are positioned as universal tools for the future and are poised to rival artists in producing visual art. This raises the intriguing question: can a mathematician create better art with mathematical equations through digital technology than an artist?

The paper has three major objectives. The first objective of the paper is to synthesize and define common categories of meta-analysis. The second objective is to propose a way to comprehend these common categories of meta-analysis through learning from their respective generic conceptual frameworks. The third objective is to point out which R packages to use to conduct a meta-analysis for each category presented in this paper. In practice, research novices may not know where to begin to learn a topic of meta-analysis for their research. Furthermore, they may not know how the topic of meta-analysis progresses from basics to advanced. This makes it almost next to impossible to use meta-analysis in their research. Certainly, this is problematic. Thus, this paper intends to help research novices overcome this problem. To achieve this, the three objectives mentioned at the beginning were implemented. To propose a synthesis of common categories of meta-analysis along with their respective definitions, a literature review was used as a method to synthesize and define the common categories of meta-analysis presented in this paper. Here, synthesizing meta-analysis categories in one figure would make it much easier for research novices to see the breadth of the subject in question. To propose an easy way to understand the concepts of meta-analysis through learning from their respective generic conceptual frameworks, personal experience was used as a method to make that offering. Here, it is encouraged that research novices should go beyond the definitions of categories of meta-analysis. They can try to frame their research by using example conceptual frameworks presented in the paper as a starting point. To point out which R packages to use with what categories of meta-analysis, a literature review was used as a method to identify those appropriate R packages available. Here, it would be useful for research novices to know which R packages they may need for their meta-analysis research. In summary, the contributions of the paper here would make research novices see and understand the common categories of meta-analysis from the bottom to the top of the pyramid, also offers an easy way to understand the common categories of meta-analysis by illustrating how to develop conceptual frameworks for the common categories of meta-analysis, and finally recommending the R packages to be used with the categories of meta-analysis. Ultimately, we hope that research novices will be able to use meta-analysis successfully in their research.

Background: The emergence of ChatGPT in the education literature represents a transformative phase in educational technology research, marked by a surge in publications driven by initial research interest in new topics and media hype. While these publications highlight ChatGPT's potential in education, concerns arise regarding their quality, methodology, and uniqueness. Objective: Our study employs unconventional methods by combining altmetrics and bibliometrics to explore ChatGPT in education comprehensively. Methods: Two scholarly databases, Web of Science and Altmetric, were adopted to retrieve publications with citations and those mentioned on social media, respectively. We used a search query, "ChatGPT," and set the publication date between November 30th, 2022, and August 31st, 2023. Both datasets were within the education-related domains. Through a filtering process, we identified three publication categories: 49 papers with both altmetrics and citations, 60 with altmetrics only, and 66 with citations only. Descriptive statistical analysis was conducted on all three lists of papers, further dividing the entire collection into three distinct periods. All the selected papers underwent detailed coding regarding open access, paper types, subject domains, and learner levels. Furthermore, we analysed the keywords occurring and visualized clusters of the co-occurring keywords. Results and Conclusions: An intriguing finding is the significant correlation between media/social media mentions and academic citations in ChatGPT in education papers, underscoring the transformative potential of ChatGPT and the urgency of its incorporation into practice. Our keyword analysis also reveals distinctions between the themes of the papers that received both mentions and citations and those that received only citations but no mentions. Additionally, we noticed a limitation that authors' choice of keywords might be influenced by individual subjective judgements, potentially skewing results in thematic analysis based solely on author-assigned keywords such as keyword co-occurrence analysis. Henceforth, we advocate for developing a standardized keyword taxonomy in the educational technology field and integrating Large Language Models to enhance keyword analysis in altmetric and bibliometric tools. This study reveals that ChatGPT in education literature is evolving from rapid publication to rigorous research.

This systematic review, adhering to the PRISMA framework, investigated the utilisation of ChatGPT, a language model developed by OpenAI, throughout Kindergarten to 12th grade (K-12) educational settings. The review synthesises findings from 13 selected papers, encompassing the strengths, weaknesses, opportunities, and threats (SWOT) analysis of ChatGPT's implementation in K-12 education, implications for various stakeholders, and practical recommendations. It is highlighted that ChatGPT could empower educators through curriculum, lesson planning, materials generation, differentiation, and optimising student learning experience through personalised learning. However, concerns regarding academic integrity and output quality must be addressed. The paper provides pedagogical recommendations and ethical considerations to utilise ChatGPT better. It contributes to the ongoing discourse about AI, particularly ChatGPT's role in K-12 education, further inspiring future research and educational practices and facilitating the effective integration of ChatGPT into K-12 educational settings where collaboration arises as a key role, in particular under the approach of co-design for learning.

In the 1970s, research on artificial intelligence in education emerged with the aim of acknowledging and accommodating the psychological aspects of the learning process. Since then, its applications have evolved and it is now used for student learning and assessment, teachers' pedagogical practice, management of educational institutions, and lifelong learning. Nevertheless, the ethical challenges of educational programmes using these systems have not been thoroughly studied. Anchored on the theoretical frame of dialogic ethics, this paper presents a section of a participatory futures research project. The goal of the research is to develop a toolkit that educators can use to ensure a smooth and ethical transition to artificial intelligence-based education while preserving the interests of educational development. This paper emphasises the need for an informed and participatory process that involves all stakeholders and begins with an expert consultation through the Delphi method, the results of which allowed the construction of eight hypothetical futures scenarios. These scenarios provide evidence that examining the ethics of using artificial intelligence systems presents an opportunity to reflect on the ethics of education as a whole. They highlight the challenge of balancing the benefits and drawbacks of such systems, especially concerning educational goals and the interplay between diverse educational actors and personal development in educational settings. The study outcomes are intended to encourage discussions on the integration of ethical artificial intelligence in education and facilitate the continuing professional development of teachers by equipping them with scenarios that can be used as a resource for training purposes.

This paper is focused on the development of a didactic proposal for English students in the Basque Country. Specifically for students in the 1st year of "Bachillerato" with Spanish and Basque as their L1 and English as their L2 which aims to improve their communication skills, especially oral abilities and pronunciation, through the use of didactic dubbing. This proposal is framed within a project-based learning (PBL), in which the final project is the creation of a vertical video dubbed in English. For this purpose, six sessions have been designed with different group activities that will culminate with the presentation of the video. This didactic unit revolves around social networks and seeks to meet the requirements of current regulations in terms of objectives, contents, competences and evaluation criteria. In addition, transversal elements such as the appropriate use of social networks, fears or interpersonal relationships are worked on. The use of videos in vertical format meets the increasing demand for creating content in this format that adapts to the new mobile devices.

Background: Driven by the ongoing need to provide high-quality learning and teaching, universities recently have shown an increased interest in using learning analytics (LA) for improving learning design (LD). However, the evidence of such improvements is scarce, and the maturity of such research is unclear. Objectives: This study is aimed to evaluate the maturity of research discussing LA-driven LD improvements in higher education. Methods: The systematic review analyses 49 empirical papers, assesses their quality and suggests further research directions. The review elaborates on methodological (research questions, strategy and methods, LA-LD integration theoretical backgrounds) and substantial (LA-driven LD improvements, types of data used, LA software development) features of the papers. Results and Conclusions: The findings demonstrated the lack of theoretical alignment between LA and LD, with research tending towards user experience studies. The most frequently used research strategy was a case study; experiments were very rare. Researchers predominantly used parsing for collecting data and AI methods for analysing it; mostly used data types related to registering learners' engagement with learning activities as well as resources and tools provided in digital learning environments. Takeaways: The research area discussing LA-driven LD improvements still has a way to go before attaining the level of full maturity. Only a third of the papers reported actual LA-driven LD improvements; moreover, only three papers measured their effectiveness. The presented LA software was mostly at the beta or implementation stages and did not assess the impact of using this software.

Recent advancements in generative artificial intelligence (GAI) have sparked debates and research on its transformative potential in education. This study explored how a specific group of educators partner with GAI tools, particularly ChatGPT, to complement and enhance their teaching. Within an interpretative paradigm, the study used an exploratory case study design to investigate how 13 educators enrolled as students in a structured master's programme at an open distance learning university in South Africa use GAI tools in their teaching. Their posts on a discussion forum were used to collect data. Findings reveal that they actively employ GAI to streamline their teaching practices, specifically to personalized tutors, simplifying English texts and translations, assessments, lesson planning, and critical thinking tasks. The study also highlights the need to understand the limitations and boundaries of GAI, including concerns about accuracy, biases and reliability. Importantly, the absence of institutional guidelines on GAI raises questions about the necessity for clear guidelines and policies to ensure responsible and ethical integration in educational contexts. The paper concludes by emphasizing the transformative potential of GAI and the fact that it will increasingly act as a partner in teaching and influence modern teaching practices. Ultimately, GAI's foothold in education is undeniable, challenging educators and policymakers to navigate its implications while maximizing its potential to support effective teaching.

Today math teachers can best reach their students and show them how math surrounds us by using manipulatives, children's literature, and GeoGebra while teaching mathematics. These are some of the best pedagogical practices for teaching mathematics today. In our high-tech world, students need to be proficient in science, technology, engineering, and mathematics (STEM) fields. As endorsed by National Council of Teachers of Mathematics (2000) and stressed in common core state standards in mathematics, it is important that we teach with technology, address dispositions and math anxiety, and make the math that young people are learning pertinent and meaningful. Frequently, it may be best to start teaching young people geometry first as opposed to numbers, which are considered more abstract and difficult to learn. Geometry is one of the most tangible divisions of math and concentrating on this first can help students' whole view of mathematics and their insolences towards learning the subject. Nowadays teachers also need to be cognizant and checking for attitudes and dispositions toward learning mathematics, as math anxiety is an issue in today's classrooms. This paper will revisit the best pedagogical practices for teaching math, the review of the use of math manipulatives, children's books, and GeoGebra to help teachers create mathematically confident young people.

Institutional discovery environments now serve as central resource databases for researchers in the academic environment. Over the last several decades, there have been numerous discovery layer research inquiries centering primarily on user satisfaction measures of discovery system effectiveness. This study focuses on the creation of a largely automated method for evaluating discovery layer quality, utilizing the bibliographic sources from student research projects. Building on past research, the current study replaces a semiautomated Excel Fuzzy Lookup Add-In process with a fully scripted R-based approach, which employs the stringdist R package and applies the Jaro-Winkler distance metric as the matching evaluator. The researchers consider the error rate incurred by relying solely on an automated matching metric. They also use Open Refine for normalization processes and package the tools together on an OSF site for other institutions to use. Since the R-based approach does not require special processing or time and can be reproduced with minimal effort, it will allow future studies and users of our method to capture larger sample sizes, boosting validity. While the assessment process has been streamlined and shows promise, there remain issues in establishing solid connections between research paper bibliographies and discovery layer use. Subsequent research will focus on creating alternatives to paper titles as search proxies that better resemble genuine information-seeking behavior and comparing undergraduate and graduate student interactions within discovery environments. [ABSTRACT FROM AUTHOR]

Landscape garden design is an important design foundation course for undergraduate students of landscape architecture in China, but the current classroom teaching has the problem of limited spatial dimension of the course. This paper proposes a virtual scene model construction method through the computer software with the creation of the need to display the scene. This virtual reality technology has immersive, interactive, and risqué characteristics. This paper focuses on the key points of virtual reality technology, discusses the specific implementation method of virtual reality technology in the "landscape garden design" course, and introduces the interactive control principle. The results of the research show that virtual reality technology to assist teaching can cultivate students' spatial thinking and enhance students' spatial simulation experience, greatly reducing the cost of traditional teaching.

Purpose: To improve the academic integrity of online examinations, digital proctoring systems have recently been implemented in higher education institutions (HEIs). The paper aims to understand how digital proctoring has been practised in higher education (HE) and proposes future research directions for studying digital proctoring in HE. Design/methodology/approach: A systematic literature review was conducted. The PRISMA procedure was adapted for the literature search. The topics were identified by topic modelling techniques from 154 relevant publications in seven databases. Findings: Seven widely discussed topics in literature were identified, including solutions for detecting cheating and student authentication, challenges/issues of uptakes and students' performance in different proctoring environments. Research limitations/implications: This paper provides insights for academics, policymakers, practitioners and students to understand the implementation of digital proctoring in academia, its adoption by HEIs, impacts on students' and educators' performance and the rapid increase in its use for digital exams in HEIs, with particular emphasis on the impacts of the systems on digitalising examinations in HE. Originality/value: This review paper has systematically and critically described the state-of-the-art literature on digital proctoring in HE and provides useful insights and implications for future research on digital proctoring, and how academic integrity in online examinations can be enhanced, along with digitalising HE.

The HCS12 microcontroller and DRAGON12-Light Trainer boards are extensively utilized in microprocessor system design education. This paper details the rationale, approach, and outcomes from implementing the DRAGON12-Light Trainer board in teaching an upper-level undergraduate microprocessors course at Oakland University. The course's primary goal is for students to acquire the skills necessary to design both hardware and software for microprocessor-based systems, with applications across various industries. The paper assesses the effectiveness of employing the Motorola HCS12 microcontroller for its real-world relevance and integrates the Freescale CodeWarrior IDE v5.1 environment for software development. This research uniquely contributes by measuring the improvement in students' practical skills, specifically in hardware and software design using assembly and C programming, through hands-on lab assignments. It reports on the development of students' abilities to engage in microprocessor-based system design and critically evaluates the applied aspects of pedagogy by incorporating the DRAGON12-Light Trainer in lab exercises, such as those involving 'Measuring Human Reaction Time' and 'Servo motor interfacing.' Quantitative results, derived from student surveys and assessments, indicate significant improvements in students' programming competencies. The paper provides statistical results showcasing the increase in students' self-rated confidence levels in assembly and C language programming before and after course completion. Additionally, qualitative insights are discussed, reflecting students' experiences and the perceived applicability of the skills they acquired. These results underscore the pedagogical value of integrating practical training devices like the DRAGON12-Light Trainer board into microprocessors curricula.

In this paper, we present the analysis of the students' achievements in learning calculus in a dynamic software environment during the COVID-19 crisis. Two groups of students, the experimental and the control one, were monitored. Blended learning was applied to the students in the experimental group, with the help of "Microsoft Teams" and dynamic software "GeoGebra," in autumn 2020. All students in the control group learned in the classroom without using "GeoGebra" in 2019. The comparison between these two groups of first-year students, regarding their calculus test results, is described in this paper. It is interesting that the results of the experimental group were significantly better than the results of students in the control group, despite the fact that the students from the experimental group learned during the COVID-19 crisis.

Employing best practices for teaching mathematics like math manipulatives, children's literature picture books, and GeoGebra can all be connected and better help to prepare students for a STEM world to develop confident young individuals in doing mathematics in a world of AI, distance learning, and online with emerging technologies. This paper will highlight all the above best practices and share how they can be connected and taught to make math meaningful and help learners be confident with technology and mathematics in a way that helps them see math in a hands-on, visual, and abstract way. Students can digitally engage with GeoGebra, Virtual and actual math manipulatives, and children's math literature/picture books. This paper will give a synopsis of the theory of these best practices, discuss manipulatives, the use of GeoGebra software in learning mathematics, and show the connections between picture books and everyday life math skills. Using such best practices in mathematics aims to develop confident young learners for a STEM and AI world where so much of the technology now focuses on AI and emerging technologies to stimulate learners, creating blazing new pathways for digital engagement in mathematics learning.

The application of computer technology has revolutionized and promoted the traditional mode of piano teaching. Nowadays, many companies and institutions have begun to apply computer technology to online piano teaching. This paper analyzes the difficulties faced by students in piano teaching and the development of piano assistant practice and summarizes the demands of parents, teachers, students, and principals for online piano teaching system. Based on this, this paper designs and implements an online piano teaching system without special hardware. This system improves the existing maximum logarithm MPa algorithm and improves the detection performance while keeping the complexity low. Combined with the special structure of parallel projection, a generalized automaton model of hybrid system is proposed, and five elements are used to describe the continuous and discrete behaviors in the hybrid subsystem. It not only keeps the advantage of low complexity of the original Max log MPa algorithm, but also obtains better detection performance.

As the cradle of cultivating talents, universities are facing great opportunities and challenges in their education. Among them, IPE (ideological and political education), as an important foundation for the future growth of university students, is of great significance. This paper discusses the relationship between IPE and psychological fitness education in university teaching. This paper expounds the necessity and feasibility of playing the role of psychological fitness education in IPECU (ideological and political education in colleges and universities). Based on this, this paper gives the strategy of infiltrating psychological fitness education into IPE. This paper combines NN (neural network) method to construct an assessment model of IPE quality. In this paper, MATLAB is used for simulation and comparative analysis. The final experiment shows that the RMSE of this algorithm is 0.512, MAE is 1.089, and the accuracy of the algorithm is 0.958.

This study explores the feasibility of using AI technology, specifically ChatGPT-3, to provide reliable, meaningful, and personalized feedback. Specifically, the study explores the benefits and limitations of using AI-based feedback in language learning; the pedagogical frameworks that underpin the effective use of AI-based feedback; the reliability of ChatGPT-3's feedback; and the potential implications of AI integration in language instruction. A review of existing literature identifies key themes and findings related to AI-based teaching practices. The study found that social cognitive theory (SCT) supports the potential use of AI chatbots in the learning process as AI can provide students with instant guidance and support that fosters personalized, independent learning experiences. Similarly, Krashen's second language acquisition theory (SLA) was found to support the hypothesis that AI use can enhance student learning by creating meaningful interaction in the target language wherein learners engage in genuine communication rather than focusing solely on linguistic form. To determine the reliability of AI-generated feedback, an analysis was performed on student writing. First, two rubrics were created by ChatGPT-3; AI then graded the papers, and the results were compared with human graded results using the same rubrics. The study concludes that e-Learning arning certainly has great potential; besides providing timely, personalized learning support, AI feedback can increase student motivation and foster learning independence. Not surprisingly, though, several caveats exist. It was found that ChatGPT-3 is prone to error and hallucination in providing student feedback, especially when presented with longer texts. To avoid this, rubrics must be carefully constructed, and teacher oversight is still very much required. This study will help educators transition to the new era of AI-assisted e-Learning by helping them make informed decisions about how to provide useful AI feedback that is underpinned by sound pedagogical principles.

The enormous developments in technology and hence the widespread use of hand-held devices in the last few decades have led to great interest in mobile-assisted language learning (MALL) as a new way of language education. MALL is considered an effective method particularly for young language learners due to their higher level of familiarity with the technology compared to their predecessors. Thus, an increasing number of mobile apps focusing on language education of children has been developed recently. It has also attracted researchers who carry out studies on MALL in terms of various perspectives such as learners' learning processes and perspectives. However, an important aspect of MALL which is the use of portable devices and apps individually out of school settings poses possible problems in terms of carrying out quasi-experimental studies. To this end, this paper combines three major areas in Applied Linguistics, namely, MALL, quasi-experimental research design, and young language learners and focuses on salient issues which are identified as lack of control, training, convenience of MALL activities for children, and ethical issues. Based on the TPACK framework, a new framework is proposed to incorporate young learners' characteristics in MALL studies. It is believed that the consideration of these points will be beneficial to future studies utilising quasi-experimental design in MALL with children.

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.

The arrival of generative artificial intelligence (AI) is fundamentally different from prior technologies used in educational settings. Educators and researchers of online, blended, and in-person learning are still coming to grips with how to employ current AI technologies in the learning experience, let alone understanding the potential consequences that future and unknown developments in AI will produce. Despite potential risks, AI may revolutionize previous models of teaching and learning, and perhaps create opportunities to realize progressive educational goals. Given the longstanding tradition of using philosophy to examine questions surrounding ethics, ontology, technology, and education, the purpose of this critical reflection paper is to draw from prominent philosophers across these disciplines to address the question: how can AI be employed in future educational contexts in a humanizing and ethical manner? Drawing from the work of Gunther Anders, Michel Foucault, Paolo Freire, Benjamin Bloom, and Hannah Arendt, we propose a framework for assessing the use and ethics of AI in modern education contexts regarding human versus AI generated textual and multimodal content, and the broader political, social, and cultural implications. We conclude with applied examples of the framework and implications for future research and practice.

Background: This paper examines the use of augmented reality (AR) as a concept-association tool in schools, with the aim of enhancing primary school students' learning outcomes and engagement. Conflicting findings exist in previous studies regarding the cognitive load of AR-enriched learning, with some reporting reduced load and others indicating increased demand and poorer performance. Understanding these implications is essential for effectively leveraging AR in education. The study offers a fresh perspective on the potential of AR technology in improving educational experiences. Objectives: The primary goal of this research was to evaluate the effectiveness of an AR-assisted concept-association strategy for improving essential knowledge acquisition and skills outcomes, increasing cognitive load, and increasing self-efficacy and learning motivation among primary school students. Methods: A quasi-experimental design with a control group was employed to investigate the effectiveness of the intervention. Results and Conclusions: The results demonstrate that the implementation of the AR-assisted concept-association strategy effectively enhanced essential knowledge acquisition and skills outcomes, increased cognitive load, and increased self-efficacy and learning motivation among primary school students. These findings highlight the potential of AR technology to improve the learning experience and engagement of primary school students. The study contributes to the existing literature on the effectiveness of AR technology in education, suggesting future research directions. Ultimately, it offers a practical solution for improving the learning experience by presenting a new approach for teaching using AR technology.

Primary school children frequently use digital devices, which can be infected by computer viruses. In this mixed methods paper with two studies (N = 278 + 114), we examined 8-year-olds' preconceptions about computer viruses and protection against them; how to teach these children about said topics using three different, 30-min-long, content-equivalent lessons; and what knowledge the children can acquire. We found that participants had limited prior knowledge of computer viruses and almost no knowledge about protection against them. However, they rarely had misconceptions. They learnt, and retained over a month, key general points and a few specific points about this domain. Acquired knowledge was still somewhat patchy, most likely represented in 'pieces' rather than as complex, theory-like chunks. Nevertheless, all three approaches produced notable learning gains (d > 1.78). A lesson organized around a narrative 5-min video and six < 1 min video snippets was the most effective: compared to a lesson organized around two 5-min videos (d = 0.89) and a teacher-led lesson without videos (d = 0.54). The findings are consistent with contemporary instructional design theories and 'knowledge in pieces' conceptual change frameworks. They imply that the topic of computer viruses should be included in second-graders' curricula.

Physical experiments in classrooms have many benefits for student learning, including increased student interest, participation and knowledge retention. While experiments are common in engineering and physics classes, they are seldom used in first-year calculus, where the focus is on solving problems analytically and, occasionally, numerically. In this paper, we detail a three-pronged lesson introducing differential equations using analytical, numerical and experimental approaches in a large first-year differential calculus course. Presenting the three approaches in succession allows students to evaluate advantages and disadvantages. The lesson incorporates software and programming and provides opportunities for active, experiential, team-based learning.

Automatic grading models are valued for the time and effort saved during the instruction of large student bodies. Especially with the increasing digitization of education and interest in large-scale standardized testing, the popularity of automatic grading has risen to the point where commercial solutions are widely available and used. However, for short answer formats, automatic grading is challenging due to natural language ambiguity and versatility. While automatic short answer grading models are beginning to compare to human performance on some datasets, their robustness, especially to adversarially manipulated data, is questionable. Exploitable vulnerabilities in grading models can have far-reaching consequences ranging from cheating students receiving undeserved credit to undermining automatic grading altogether--even when most predictions are valid. In this paper, we devise a black-box adversarial attack tailored to the educational short answer grading scenario to investigate the grading models' robustness. In our attack, we insert adjectives and adverbs into natural places of incorrect student answers, fooling the model into predicting them as correct. We observed a loss of prediction accuracy between 10 and 22 percentage points using the state-of-the-art models BERT and T5. While our attack made answers appear less natural to humans in our experiments, it did not significantly increase the graders' suspicions of cheating. Based on our experiments, we provide recommendations for utilizing automatic grading systems more safely in practice.

In Victoria, Australia, senior secondary mathematics students are expected to use technology and thus need to make decisions about using pen-and-paper (P&P) or technology when solving mathematics problems. The predominant technology is a Computer Algebra System (CAS). This study investigated the beliefs about CAS held by twelve Year 11 students as they learnt to use CAS and whether these beliefs were stable over time. These students held a range of beliefs related to the usefulness of CAS, speed of CAS compared to P&P, whether CAS is proper mathematics, choice of CAS or P&P, ease of use, the correctness of answers and solving problems in Mathematical Methods (i.e. the mathematics subject studied). Beliefs are often described as being stable (e.g. McLeod, 1992), but some researchers stress stability needs to be determined empirically rather than being seen as a characteristic of beliefs (e.g. Liljedahl et al., 2012). For this sample of students, stability (rather than instability) is a feature of students' beliefs about CAS.

The popularity of social networks turns them into a legal method for promoting a healthy lifestyle, which benefits not only people but also different countries' governments. This research paper aimed to examine the Keep fitness app integrated into WeChat, Weibo and QQ as regards long-term improvements in health-related behaviors (physical activity, nutrition, health responsibility, spiritual growth, interpersonal relationships and stress management) and assess the associated risk of increased social media addiction. Students from Lishui University in China (N = 300) participated in this study, and they were formed into control and experimental groups. The Healthy Lifestyle Behavior Scale and Social Media Disorder Scale were used as psychometric instruments. The Keep app was found to improve respondents' scores on the parameters of physical activity, nutrition and health responsibility (P = 0.00). However, the level of dependence on social media did not change in either the control or the experimental group during the year of research (P [greater than or equal to] 0.05). It is concluded that fitness apps can be an effective tool to promote healthy lifestyles among young people in China and other countries. The feasibility of government investment in fitness apps to promote healthy lifestyles is substantiated.

Over the past two decades, there has been an explosion of innovation in software tools that encapsulate and expand the capabilities of the widely used student peer assessment. While the affordances and pedagogical impacts of traditional in-person, "paper-and-pencil" peer assessment have been studied extensively and are relatively well understood, computerized (online) peer assessment introduced not only shifts in scalability and efficiency, but also entirely new capabilities and forms of social learning interactions, instructor leverage, and distributed cognition, that still need to be researched and systematized. Despite the ample research on traditional peer assessment and evidence of its efficacy, common vocabulary and shared understanding of online peer-assessment system design, including the variety of methods, techniques, and implementations, is still missing. We present key findings of a comprehensive survey based on a systematic research framework for examining and generalizing affordances and constraints of online peer-assessment systems. This framework (a) provides a foundation of a design-science metatheory of online peer assessment, (b) helps structure the discussion of user needs and design options, and (c) informs educators and system design practitioners. We identified two major themes in existing and potential research--orientation towards scaffolded learning vs. exploratory learning and system maturity. We also outlined an agenda for future studies.

We are developing a deterministic fluidic assembly process which uses software to sense, sort and assemble micron‐scale devices into desired patterns. This process can be adapted to assemble large numbers of microLEDs (uLEDs) over large areas with high throughput and nearly perfect yield. We present data demonstrating the precision and reliability of the process to illustrate its potential as a scalable process for manufacturing uLED displays. [ABSTRACT FROM AUTHOR]

Background: Stroke profoundly affects quality of life (QOL), including loss of employment, reduced social activity, shrinking social networks and low mood. Dysgraphia (impaired writing) is a common symptom of aphasia yet is rarely targeted in rehabilitation. Recent technological advances might challenge this, since much communication is now conducted digitally through writing. The rehabilitation of writing may therefore help to address the wider consequences of stroke and aphasia. Aims: Can assistive technology (AT) training for people with dysgraphia: (1) improve written output, and are gains achieved only with AT? (2) improve reading comprehension scores, and are gains achieved only with AT? and (3) affect social participation, mood or QOL. Methods and Procedures: Design: A mixed-methods, repeated measures, small group study design was adopted (qualitative outcomes will be reported elsewhere). Participants: Recruited from community settings, for example, Stroke Association communication support groups. Inclusion criteria: over 18 years old, aphasia due to stroke, acquired dysgraphia, writing more impaired than speech, fluent English prior to stroke, access to computer and Internet. Exclusion criteria: currently receiving speech and language therapy, significant cognitive impairment, neuromuscular/motor-speech impairments/structural abnormalities, developmental dyslexia, uncorrected visual/auditory impairments. Procedures: Screening and diagnostic assessments at time T1 (first baseline). Outcome measures at T1; repeated at T2 (second baseline), T3 (end of intervention), T4 (3-month follow up). Social participation assessment and cognitive monitoring at T2, T3, T4. Intervention: Seven-ten hours individual therapy weekly and additional email support. Participants were trained to operate Dragon NaturallySpeaking (speech to text package) and ClaroRead (read writing aloud). Outcome measures were administered on pen and paper (control) and on computer, with AT enabled only at T3, T4. Outcomes and Results: Computer narrative writing was significantly improved by AT training (Friedman's X[superscript 2] (3) = 8.27, p = 0.041), indicating a compensatory effect of AT. Though reading comprehension significantly improved in the computer condition (Friedman's X[superscript 2] (3) = 21.07, p = 0.001), gains could not be attributed to the AT. Gains were achieved only when measures were administered on the keyboard, with AT enabled. Thus, a compensatory rather than remediatory effect was suggested. Social network size significantly increased; there were no significant changes in mood/QOL. Individual success rates varied. Conclusion and Implications: The customisable AT training was acceptable to participants and resulted in significantly improved narrative writing. Compensatory AT interventions are a useful adjunct to remediatory writing interventions and may particularly support functional writing.

Qualitative comparative analysis (QCA) is a hybrid method designed to bridge the gap between qualitative and quantitative research in a case-sensitive approach that considers each case holistically as a complex configuration of conditions and outcomes. QCA allows for multiple conjunctural causation, implying that it is often a combination of conditions that produces an outcome, that multiple pathways may lead to the same outcome, and that in different contexts, the same condition may have a different impact on the outcome. This approach to complexity allows QCA to provide a practical understanding for complex, real-world situations, and the context of implementing interventions. There are guides for conducting QCA in primary research and quantitative systematic reviews yet, to our knowledge, no guidance for conducting QCA in systematic mixed studies reviews (SMSRs). Thus, the specific objectives of this paper are to (1) describe a step-by-step approach for novice researchers for using QCA to integrate qualitative and quantitative evidence, including guidance on how to use software; (2) highlight specific challenges; (3) propose potential solutions from a worked example; and (4) provide recommendations for reporting.

Researchers in second language (L2) and education domain use different statistical methods to assess their constructs of interest. Many L2 constructs emerge from elements/parts, i.e., the elements "define" and "form" the construct and not the other way around. These constructs are referred to as emergent variables (also called components, formative constructs, and composite constructs). Because emergent variables are composed of elements/parts, they should be assessed through confirmatory composite analysis (CCA). Elements of emergent variables represent unique facets of the construct. Thus, such constructs cannot be properly assessed by confirmatory factor analysis (CFA) because CFA and its underlying common factor model regard these elements to be similar and interchangeable. Conversely, the elements of an emergent variable uniquely define and form the construct, i.e., they are not similar or interchangeable. Thus, CCA is the preferred approach to empirically validate emergent variables such as language skills L2 students' behavioral engagement and language learning strategies. CCA is based on the composite model, which captures the characteristics of emergent variables more accurately. Aside from the difference in the underlying model, CCA consists of the same steps as CFA, i.e., model specification, model identification, model estimation, and model assessment. In this paper, we explain these steps. and present an illustrative example using publicly available data. In doing so, we show how CCA can be conducted using graphical software packages such as Amos, and we provide the code necessary to conduct CCA in the R package lavaan.

In this paper we review selected significant developments in the use of digital technology in the teaching and learning of mathematics over the last five years. We focus on a number of important topics in this field, including the evolvement of STEAM and critical making as well as the process of redefining learning spaces in the transformation of the mathematics classroom. We also address the increasing use of computer algebra systems and dynamic geometry packages; and the issue of student collaboration online, especially using learning environments and social media. We briefly touch on artificial intelligence systems, including hyper-personalisation of learning, multimodality and videos. We include a brief discussion on the impact of COVID-19 on mathematics education, and lastly on the more theoretical perspective of the epistemology of digital technology and the construct of humans-with-media. We conclude the discussion with some possible concerns and mentioning some possible new topics for research in the field.

Purpose: This purpose of this paper is to report on the development of an evidence-informed framework created to facilitate the formulation of generative artificial intelligence (GenAI) academic integrity policy responses for English medium instruction (EMI) higher education, responding to both the bespoke challenges for the sector and longstanding calls to define and disseminate quality implementation good practice. Design/methodology/approach: A virtual nominal group technique engaged experts (n = 14) in idea generation, refinement and consensus building across asynchronous and synchronous stages. The resulting qualitative and quantitative data were analysed using thematic analysis and descriptive statistics, respectively. Findings: The GenAI Academic Integrity Policy Development Blueprint for EMI Tertiary Education is not a definitive mandate but represents a roadmap of inquiry for reflective deliberation as institutions chart their own courses in this complex terrain. Research limitations/implications: If repeated with varying expert panellists, findings may vary to a certain extent; thus, further research with a wider range of stakeholders may be necessary for additional validation. Practical implications: While grounded within the theoretical underpinnings of the field, the tool holds practical utility for stakeholders to develop bespoke policies and critically re-examine existing frameworks. Social implications: As texts produced by students using English as an additional language are at risk of being wrongly accused of GenAI-assisted plagiarism, owing to the limited efficacy of text classifiers such as Turnitin, the policy recommendations encapsulated in the blueprint aim to reduce potential bias and unfair treatment of students. Originality/value: The novel blueprint represents a step towards bridging concerning gaps in policy responses worldwide and aims to spark discussion and further much-needed scholarly exploration to this end.

Purpose: Mobile learning has seen tremendous growth over the years. Like any other software application, usability is one of the key concerns in its successful implementation. There is a lack of study that provides a comprehensive overview of usability testing of mobile learning applications. Motivated by this a mapping study is conducted. Design/methodology/approach: A systematic mapping study was conducted using 51 papers retrieved from the Scopus database published between 2005 and 2022 that reported on usability testing of mobile learning applications. Findings: The key findings suggest that research is expected to expand in the near future. User-based testing is the commonly used method, while data are collected mainly through questionnaires, observation and interviews. Testing is mainly conducted in a controlled environment. Originality/value: The study provides (1) an evidence-based discussion on usability testing of mobile learning applications, (2) an up-to-date map on state of the art on usability testing of mobile learning applications and (3) providing direction for further research to scientifically strengthen the field.