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The Objective of the study was to ascertain how Rosetta Stone in English class affected students' ability to pronounce English. This methodology employed a quasi-experimental pretest-posttest design with 30 samples split into two groups. The technique used was non-probability sampling. Pronunciation test technique was employed for data collecting. To compare Pronunciation abilities before and after using Rosetta Stone, data analysis used a t-test. The findings demonstrated that the t-Stat statements derived from the material discussed had value. A key element of Pronunciation is the students' capacity to pronounce the words and sentences that are practiced by imitating the application. The findings of this study have significance for students' knowledge of how to pronounce the words and sentences correctly and influence their level of pronunciation. The findings demonstrated that the t-test-table value was (1,699 < 9,950 > 1,331), indicating that Rosetta Stone gave a significant effect on treatment class on students' pronunciation. This result finding recommend other researcher to use the Rosetta Stone as an alternative media used for future.c

Research has suggested that the type of feedback learners receive can impact on whether learners understand the feedback, the extent to which they engage with it, and whether they incorporate it in their revised drafts. However, to date, only a small number of studies have investigated learner engagement with corrective feedback provided by automated writing evaluation tools, and of those few have considered in greater depth the impact of the type of automated feedback on engagement. This multiple-case study examines two EFL learners' engagement with the two forms of corrective feedback provided by Criterion categorised as generic and specific and factors that can explain the nature of their engagement. Data were collected from learners' first and revised drafts of multiple essays on Criterion, screencasts of students' think-aloud procedures while revising essays, and stimulated recall interviews. Findings indicate the learners' higher uptake rate and more successful error corrections in response to generic versus specific feedback. However, their mental effort expenditure differed when cognitively engaging with the feedback, which could be explained in terms of individual learning goals, feedback quality, and the nature of tagged errors. These findings have relevant implications for utilising automated corrective feedback in L2 writing classes.

The increasing popularity of mobile dual-language learning applications (apps) in the education industry has led to a growing interest in investigating their effectiveness. However, there is a paucity of research investigating mobile apps specifically designed for speakers learning dual languages. Accordingly, this study utilised a qualitative research method to explore the perceptions and design of such apps for bilingual teaching and learning. A cohort of 30 in-service teachers participated in individual interviews, serving as the primary source of data collection and analysis. Thematic analysis was employed to identify patterns within the transcripts and detailed field notes. The results revealed nine themes and twenty-four subthemes related to mobile dual-language learning apps. These findings provide insights into the potential benefits and challenges of using mobile apps for language learning, as well as implications for the design of dual-language learning apps. The implications of this study extend beyond the education industry and have practical implications for language educators and app designers alike. The study contributes to the current body of knowledge in the field of education by shedding light on the effectiveness of mobile dual-language learning apps for bilingual teaching and learning.

The main objective of this article is to highlight the importance of training in digital tools at the university level to foster the development of innovative and efficient data analysis from a scientific perspective. In an increasingly digitised world, the acquisition of digital skills has become a fundamental requirement for success in various disciplines, especially in conducting academic studies and research. The implementation of Computer-Aided Qualitative Data Analysis (CAQDAS) software, such as the ATLAS.ti platform, for text analysis not only enriches the educational experience but also prepares students to excel in an ever-evolving digital environment and raise the quality of their research. We present here a practical example of textual analysis in ATLAS.ti that can serve as a reference guide for similar studies based on content analysis of interviews. Various qualitative and quantitative data analysis options and techniques are explored that allow researchers to identify patterns, trends and relationships in the texts analysed, which contributes to a deeper understanding of the topics under study by transcending traditional methods of text analysis.

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.

GPT-3 is a neuronal language model that performs tasks such as classification, question-answering and text summarization. Although chatbots like BlenderBot-3 work well in a conversational sense, and GPT-3 can assist experts in evaluating questions, they are quantifiably worse than real teachers in several pedagogical dimensions. We present the first systematic literature review that analyzes the main contributions and uses of GPT-3 in the field of education. The protocols suggested in the PRISMA 2020 statement were followed for the drafting of the review. According to the results, 34 significant productions were identified through a systematic search in ISI Web of Science, SCOPUS and Google Scholar. GPT-3 has been considered in the academic, ethical and medical fields, in humanities and in computer science, in the formulation of questions and answers, and through cooperative educational dialogs. GPT-3 has been proven to have valuable applications in education, such as the automation of routine tasks, in making quick diagnoses of the students' weaknesses and in the automatic generation of questions, but it still faces challenges and limitations that require additional investigation. We discuss the educational possibilities and the limitations to the use of GPT-3.

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 is a popular technology that affects many fields today. Its impact is noticeable daily, especially in education, design, and other essential areas. This technology, which stands out with the convenience it creates for users and its help in organizing the workflow, will play a critical role in our lives in the future. This study is aimed to touch upon the innovations provided by artificial intelligence in the field of education and some of its basic features. Some software, especially ChatGPT, is included based on the potential of language models and their essential usage areas. In addition, in the study, it is mentioned how artificial intelligence can be evaluated as a material in text and visual form, the essential functions of artificial intelligence technologies, which are rapidly changing today, are explained in this article, and stimulating points about the current situation are mentioned for those who will work in the field.

Complex Dynamic Systems Theory (CDST) has significantly reshaped our understanding of second language learning and teaching across the micro, meso, and macro levels for several decades since its systematic introduction by Larsen-Freeman and Cameron (2008). This study conducts a comprehensive review of empirical research on second language learning and teaching within the framework of CDST from 2008 to 2022. Textometric analysis, a computer-supported qualitative method for textual analysis, was employed to conduct the review by using the IRAMUTEQ software. A total of 198 publications were identified, leading to the emergence of three prominent research themes: research on the language system, research on the second language learner and learning, and research on the second language teacher and teaching. The chronological trends and distinguishing features of the three research themes were discussed against a mainstream model of language learning and teaching at the micro, meso, and macro levels (The Douglas Fir Group, 2016). The findings of this review hold the potential for enlightening future research endeavors in this field.

This practice report describes a 12-week stand-alone course designed to address the challenges university students face in foreign language classes. Adopting principles of transformative language learning, course content, activities and resources were designed and implemented to dispel myths and preconceptions regarding language instruction, promote self-directed, independent learning, and raise awareness of the cognitive and socio-emotional processes involved in language learning. Students' feedback indicate that the course had a significant impact on their perceptions of adult language learning, their capacity to reflect on their use of strategies, and the importance of developing a plan to continue applying these new understandings in their academic pursuits. The course fostered a shift in students' perspective, from viewing themselves as passive recipients of 'language injections', to becoming self-directed, motivated, and independent learners.

This study looked at how practically ASR-based interactive video assignments can be integrated into EFL classrooms for additional out-of-class speaking practice, and what effects it will have on students. We created an ASR-based interactive video assignment using Google Scripts and gave it to students as a homework assignment between lessons in which students were learning to give opinions and respond to interrogatives. We used pre- and post-treatment surveys to examine shifts in students' attitudes and gather their responses pertaining to the interactive video assignment. In general, students thought the assignment was good practice, and the majority of students showed positive shifts in confidence and feelings of liking English (p < 0.001 for both, rs = 0.28 and rs = 0.48, respectively). However, we found that students overfocused on pronunciation, especially when they were more proficient learners (p = 0.035, r = 0.015). Therefore, we believe that while the activity was somewhat successful, the feedback likely needs to be changed for ASR-based systems when the focus should not be pronunciation. Future possibilities are also discussed.

This article aims to analyze the process of the progressive mathematization of the concept of the function by secondary school students through the resolution of a free fall situation. A free-fall mathematical situation was designed and experimented using the Tracker software to obtain movement data. Worksheets and conversations were collected between students and the teacher, and experiments and class discussions were filmed. The analysis consisted of identifying the development from informal strategies associated with the context of the problem to formal procedures. The results show that the process brings meanings to mathematical ideas such as variation, variables, and the relationship between variables, allows coordination and transition between representations and contributes to the learning of functions as a representation of the variation of physical phenomena.

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

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.

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?

This quantitative study is designed to help educational institutions and instructors make informed decisions regarding the use of online proctoring software. The researchers studied the impact of proctoring software in online courses by comparing the final grades of two groups of online, undergraduate students who took the same online course with the same professor who administered virtually the same content, with and without proctoring software. The overall sample included 252 students in six different undergraduate courses. When regressing all six courses together, the data did not show that the addition of proctoring software created a significantly lower course grade. The researchers then regressed the data of each individual undergraduate course, with the addition of independent variables, which had a mixed-bag of results. Interestingly, an undergraduate business course showed that the use of proctoring software reduced course grades while other courses produced interesting significant findings relative to gender and attendance status.

A qualitative documentary research of software and multimedia artificial intelligences was chosen to enable the ontological understanding of the object of study that aims to explore the potential for learning through Artificial Intelligence (AI). Besides, it is shown how AI, as a novel component within the digital education landscape, contributes to educational technological capital. In light of our research area, the focus is on determining how AI can be harnessed to enhance the development of communicative competence. This entails identifying and categorizing AI tools relevant to language didactics. The sample size for this study is 120 AI applications, sourcing and compiling data on AI tools from specialized websites. This sample serves as a paradigm justifying a mixed-method study capable of combining quantitative and qualitative data. The variables supporting this study consider four characteristics related to the specific typify of the digital tool in the pedagogical context. The first aspect pertains to the classification of generative AI tools with potential educational use. AI tools enriches and enhances the dimensions of learning underscoring the urgent need for literacy in this technology. The second variable was linked to the Pedagogical Competences of Teachers and the areas. The category in language education pertains to educators' ability to create, adapt, and employ digital resources that enhance language teaching and learning. The third and fourth identify the skills related to language learning. In the context of learning environments with AI tools, it is essential to contemplate the role of linguistic competence as subordinate to communicative competence.

There is currently little research on the use of podcasting as a learning tool in the tertiary-level foreign language classroom. Here we attempt to partly fill that gap through a multiple case study of three university teachers who use this technology in their courses in Spanish as a foreign language. We obtained data for analysis primarily in two ways, through individual interviews with the teachers on two separate occasions and by compiling a corpus of all the podcasts (227 in total) the three teachers had used in their classes, most of them student-created. Given this last fact, our analysis focuses especially on the creation of podcasts by students and the teacher's role in the process. Our findings show that podcasting as an educational tool can offer significant benefits for language learning. Student-produced podcasts, in particular, provide students with a context to practice their foreign language speaking skills, but reading and writing skills are also heavily engaged in the process of writing a script for what they will say in their podcast. The production of podcasts can also be highly motivating for students if the final product is posted online on an open-access host site; this means that native speakers of the target language could be among the potential listeners. On the downside, the process of having students produce podcasts requires considerable time and preparation on the part of both teachers and students, and students who are shy or less comfortable with technology may find the task challenging.

This study strived to determine the effectiveness of integrative teaching of reading strategies and working memory on basic math and problem-solving skills. It is a quasi-experimental pretest-posttest study carried out on 50 second-graders from Chahashk Shandiz village in the academic year of 2020-2021 who were randomly selected by cluster sampling and randomly divided into experimental and control groups. The intervention was performed by cellphone software through virtual education in two steps. The experimental group underwent integrative training for 20 sessions, but the control group did not receive any intervention. Colored Progressive Matrices (1956) and a researcher-made story problem-solving and arithmetic skills test were used to collect data. Descriptive statistics (mean and standard deviation) and one-way multivariate analysis of covariance were used to analyze the data. The results showed that the integrative teaching of reading strategies and working memory enhanced the basic math skills, with an effect size of 0.67 in second-grade elementary students (P<0.001). According to the results, integrative teaching improved students' problem-solving skills but did not affect their skills in arithmetic operations. Therefore, second-grade elementary teachers can use this method to enhance their story problem-solving skills.

Nowadays, it is not common to come across sectors that can work and succeed without using information technology (IT). IT has now become a part of organizations' management and strategic decision-making mechanisms. Therefore, organizations make serious investments to develop software and system infrastructures and transfer business processes to digital environments such as cloud technologies. So, they get business resilient while they gain opportunities of spreading their business activities to wide stakeholders and customers. Here it is essential to provide assurance that an organization evaluates, develops, implements, maintains, disposes of its IT systems and manages life cycle of all information and information technology systems properly on all physical and logical environments. However, the most important matter is that an organization's objectives are met by the management practices of its information systems. While organizations carry out their digital transformation at a dizzying speed, organizations should be protected from material and moral damages such as loss of money, loss of commercial confidential information, loss of reputation by protecting the confidentiality, integrity and accessibility of the information assets they own and also comply with legal legislations such as the protection of personal data and intellectual property rights. In order to achieve this, organizations benefit from one or more frameworks, standards or directives that are suitable for their own structures and needs, separately or by integrating them. However, the most important point in this transformation process is the transition of organizations from IT management understanding to IT governance. In other words, IT has now become a part of the strategic decision-making mechanism by rising from the level of support tool to carry out business processes in organizations. When we search the academic literature, frameworks and standards, it is seen that the key elements of IT governance are structures, processes and relational mechanisms. If these three components are set up correctly and fit the organization, it can be assured that IT aligns with and supports the objectives of the organizations and that effective IT governance is achieved in every kind of organizations. In this research, databases such as Web of Science, IEEE, and SCOPUS are scanned and found articles are reviewed in order to make a literature review on IT Governance Mechanisms and Frameworks. Results are evaluated and discussed. By the way, suggestions for further studies were made and advanced researches were shed light on.

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.

This study delves into the implications of incorporating AI tools, specifically ChatGPT, in higher education contexts. With a primary focus on understanding the acceptance and utilization of ChatGPT among university students, the research utilizes the Unified Theory of Acceptance and Use of Technology (UTAUT) as the guiding framework. The investigation probes into four crucial constructs of UTAUT--performance expectancy, effort expectancy, social influence and facilitating conditions--to understand their impact on the intent and actual use behaviour of students. The study relies on data collected from six universities in two countries and assessed through descriptive statistics and structural equation modelling techniques, and also takes into account participants' gender and study level. The key findings show that performance expectancy, effort expectancy, and social influence significantly influence behavioural intention. Furthermore, behavioural intention, when considered alongside facilitating conditions, influences actual use behaviour. This research also explores the moderating impact of gender and study level on the relationships among these variables. The results not only augment our comprehension of technology acceptance in the context of AI tools but also provide valuable input for formulating strategies that promote effective incorporation of ChatGPT in higher education. The study underscores the need for effective awareness initiatives, bespoke training programmes, and intuitive tool designs to bolster students' perceptions and foster the wider adoption of AI tools in education.

As computational thinking becomes increasingly essential, the challenge of designing effective teaching approaches to foster students' abilities in this area persists, especially for higher order thinking skills. This study addresses this challenge by proposing and implementing a peer assessment-based Scrum project (PA-SP) learning approach in university computer programming learning. The approach involves dividing 98 third-year college students into an experimental group, which employs the PA-SP approach, and a control group, which uses the conventional Scrum project (C-SP) approach. The study investigates the impact of the PA-SP approach on students' programming projects, learning motivation, project collaboration, communication, critical thinking, and cognitive load. The results demonstrate that the PA-SP approach significantly enhances learning achievement and motivation while also improving collaboration, communication, and critical thinking. By providing practical insights into effective teaching strategies to promote students' computational thinking, this research has significant implications for improving programming education in universities.

Artificial intelligence (AI) technologies have the potential to completely transform how we teach and learn in higher education. ChatGPT, a language model developed by OpenAI, is one such tool that can deliver individualized recommendations to students, increase collaboration and communication, and improve student learning results. However, there are some obstacles to overcome, such as ethical concerns and implementation issues. This study reviews related work on the use of artificial intelligence in education, with a focus on ChatGPT and its possible applications in higher education. It also examines the benefits and drawbacks of adopting ChatGPT in higher education, as well as implementation advice. Finally, the report discusses future directions for ChatGPT research in higher education. According to the findings of this paper, ChatGPT represents a significant opportunity for higher education institutions to improve the quality and accessibility of education; however, its implementation must be approached with caution and a clear understanding of the opportunities and challenges involved.

Teacher-student interaction (TSI) is essential for learning efficiency and harmonious teacher-student interpersonal relationships. However, studies on TSI support tools often focus on teacher needs while neglecting student needs and autonomy. To enhance both lecturer competence in delivering interpersonal interaction and student autonomy in TSI, we developed NaMemo2, a novel augmented-reality system that allows students to express their willingness to TSI and displays student information to teachers during lectures. The design and evaluation process follows a new framework, STUDIER, which can facilitate the development of theory-based ethnics-aware TSI support tools in general. The quantitative results of our four-week field study with four classes in a university suggested that NaMemo2 can improve (1) TSI in the classroom from both teacher and student perspectives, (2) student attitudes and willingness to TSI, and (3) student attitudes to the deployment of NaMemo2. The qualitative feedback from students and teachers indicated that improving TSI may be responsible for improved attention in students and a better classroom atmosphere during lectures.

With the rapid growth of web applications, web application security (WAS) has become an important cybersecurity issue. For effective WAS protection, it is necessary to cultivate and train personnel, especially beginners, to develop correct concepts and practical hands-on abilities through cybersecurity education. At present, many methods offer vulnerable web environments to support practical hands-on training, including large-scale "Capture the Flag" mode (e.g., Cyber Range), pre-configured virtual machine images (e.g., Mutillidae), pre-built stand-alone applications (e.g., WebGoat), and web-based system (e.g., Damn Vulnerable Web Application). However, beginners need not only hands-on training tools and systems but also assistance to support effective learning. Moreover, pre-built training content and exercises are usually not easy to modify and thus lack the flexibility to meet specific teaching needs. Therefore, this study proposed and developed the Web-based Hands-On Learning Environment (WebHOLE) to efficiently assist beginners in learning WAS. To improve the flexibility of the training content, a web-based authoring tool was developed in WebHOLE to create customized hands-on learning exercises. Accordingly, learners can learn and practice the WAS training content online with learning assistance provided by the hands-on learning system. The hands-on abilities of the learners can be efficiently assessed by the hands-on testing system using online exams with progressive hints and automatic grading. Furthermore, to improve the effectiveness of teaching and testing, a portfolio analysis scheme using a data mining technique was developed to identify learning barriers and problematic test items. WebHOLE was applied to an actual beginner-level WAS course for undergraduate students. The experimental results showed the benefits of WebHOLE on WAS learning, with a significant improvement in learning outcomes. Students expressed high satisfaction with WebHOLE's learning assistance, rating it with average satisfaction scores above 4.0 out of 5.0. The portfolio analysis scheme also showed the effectiveness of WebHOLE in identifying learning problems and refining test items.

Background: Engineering curricula are built around faculty and accreditors' perceptions of what knowledge, skills, and abilities graduates will need in engineering careers. However, the people making these decisions may not be fully aware of what industry employers require for engineering graduates. Purpose/Hypothesis: The purpose of this study is to determine how industry employer-sought professional and technical skills vary among engineering disciplines and levels of education. Design/Method: Using a large sample (n = 26,103) of mined job advertisements, we use the O*NET skills database to determine the frequencies of different professional and technical skills for biomedical, civil, chemical, electrical, environmental, and mechanical engineers with bachelor's, master's, and PhD degrees. Results: The most frequently sought professional skill is problem-solving; the most frequently sought technical skills across disciplines are Microsoft Office software and computer-aided design software. Although not the most frequently requested skills, job advertisements including the Python and MATLAB programming languages paid significantly higher salaries than those without. Conclusions: The findings of this study have important implications for engineering program leaders and curriculum designers choosing which skills to teach students so that they are best prepared to get and excel in engineering jobs. The results also show which skills students can prioritize investing their time in so that they receive the largest financial return on their investment.

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.

When performing an aggregate data meta-analysis of a continuous outcome, researchers often come across primary studies that report the sample median of the outcome. However, standard meta-analytic methods typically cannot be directly applied in this setting. In recent years, there has been substantial development in statistical methods to incorporate primary studies reporting sample medians in meta-analysis, yet there are currently no comprehensive software tools implementing these methods. In this paper, we present the "metamedian" R package, a freely available and open-source software tool for meta-analyzing primary studies that report sample medians. We summarize the main features of the software and illustrate its application through real data examples involving risk factors for a severe course of COVID-19.

Social networking service platforms and social media have generated a significant amount of discussion and debate regarding their use in second or foreign language learning. However, leveraging digital tools with pedagogical constructs to enhance accessibility and engagement in teaching and learning English idiomatics has not received equal attention. Consequently, discovering new modes of learning in the formulation and implementation of English idiomatics instruction in the modern digital age is a must. This study investigated Chinese EFL learners' development of idiomatic competence. It examined English vivid phrasal (VP) idiom learning in classrooms supporting WeChat use vis-à-vis the classrooms supporting traditional modes of language instruction without WeChat. It also explored the extent to which WeChat-enhanced instruction can affect learners' motivation, attitude, collaboration, and experience. The participants of this study were 55 first-year English major students who took part in an intensive, eight-week idiom-training module. A pre- /post-test and a questionnaire were used to uncover how a WeChat-driven learning environment can mediate the way students acquired Post-Lexical Level (PLL) VP idioms and what their general perceptions towards using WeChat in the idiom-oriented activities were. The results indicated that there was a statistically significant difference in achievement between the WeChat-enhanced group and the traditional face-to-face group. Analysis of data further revealed that peer interactions supported by WeChat are likely to be essential learning constructs in furthering idiomatic competence development.

Many universities have, or are facing, the task of providing high quality essential customer services with fewer financial and human resources. The growing diversity of students, their needs and proficiencies, along with the increasing variety of university program offerings, make providing customized, ondemand, automated solutions crucial to delivering high quality customer service. In this paper, the authors describe the development of an artificial intelligence-backed chatbot to aid in answering student advisement questions. The main objective of the chatbot is to provide 24/7 supplemental program specific advising assistance to graduate computer science majors to lessen the advising load of university faculty. In this paper, the authors describe the development of a chatbot prototype using IBM Watson Assistant running on a specially designed website and Slack platform to address frequently asked questions of computer science graduate students. Results of a pilot study conducted on 99 graduate students indicate that the prototype is a positive step forward in making student advising accessible, usable, and scalable to a broader audience.

Educators are tasked with continually updating course objectives, content, assignments, and assessment to meet model curriculum guidelines. IS2020 proposes program level outcomes for required and elective areas. Two elective areas in IS2020 are Data and Business Analytics and Data and Information Visualization. IS2020 details 14 program level competencies (organized within knowledge elements and skills) that are then integrated into individual course-level design. This work presents a set of laboratory exercises to fulfill the competencies of both elective areas. The set of exercises have been taught in the classroom over several years and have been refined to evaluate coverage of the 14 program competencies. The exercises begin with step-by-step tutorials that build student capabilities with software. Advanced exercises propose open challenges to solve. These resources provide IS programs with a draft of potential exercises to include in courses and a framework for covering program-level objectives.

Students today are often assumed to be digitally literate and prepared for college, but that is not always the case. Introductory technology courses at the college level provide students with the technology and digital skills necessary for them to succeed in college. This study, conducted at a small New England "business school" that also offers liberal arts degrees, sought to determine which digital skills most incoming first-year college students have. The study examines which digital skills and competencies students acquire before entering college, and if those skills are adequate to achieve academic success in college. Guiding the study are these research questions: (RQ1) What digital devices are students using and with what frequency? (RQ2) What digital literacy competencies (DLCs) do incoming first-year students possess? (RQ3) How do first-year students self-rank their DLCs, and how does that compare to individual task self-ranking? (RQ4) What DLCs do incoming first-year students perceive as the most important to know, and do they have those skills? Results showed that most K-12 students are learning on Macs or Chromebooks and use the Google Suite of applications. They may need to switch to Windows machines and/or Microsoft Office products upon entering college. Almost 16% reported taking no high school courses covering computer topics. Only 34% of incoming first-year college students consider themselves "tech savvy" and most rank themselves at the beginner or intermediate level on digital literacy competencies. They believe the three most important skills to know are spreadsheets, programming, and word processing.

English language education nowadays is not merely about the instruction and acquisition of linguistic knowledge and skills. Instead, it has progressed to the real-life applications of the target language, which further requires a mastery of cultural knowledge and skills. In terms of culture, English as a Foreign Language (EFL) learners, compared to native speakers of English, own their unique native culture. Yet, since language teachers tend to focus on the delivery of English cultural knowledge, EFL learners' native culture is sometimes shadowed in the mainstream English classrooms worldwide. To this end, this exploratory paper aims to advocate attention to the importance of EFL learners' native culture awareness and share some practical teaching and learning experiences in an English course called "Multimedia and Foreign Language Learning." The paper outlines the pedagogical design of the course in China, providing classroom examples and practical suggestions to course designers, educators and instructors. We expect to give insights into integrating native culture into foreign language education in university settings.

Many small organizations sponsor events and activities that could benefit from the data management and reporting capabilities provided through a centralized database. However, many of those organizations do not have the budget to afford a commercial solution or an on-going subscription to a cloud-based solution for a small scope event with limited frequency use. The case focuses upon a service project volunteer management system for recording, managing, and reporting on volunteers and the service projects they are doing. The case provides a realistic scenario that can be used in a systems analysis and design, database development, or graduate level management information systems course. Multiple assignment options are provided allowing instructors to select an assignment based upon course material coverage. Suggested assignments include the development of process modeling diagrams such as a data flow or swim lane diagrams and database design and development artifacts.

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 Fourth Industrial Revolution (4IR) refers to the current rapid technological growth that fundamentally changes how humans live. 4IR is essential to improve higher education worldwide and develop crucial skills such as e-learning and innovation, information and media technology, and life and career skills. This study aims to assess the knowledge of the application of the nine pillars of 4IR in Education among STEM teachers in Malaysia. The nine primary pillars of 4IR that create a digital revolution in industries are autonomous robots, augmented reality (AR), system integration, additive manufacturing, cybersecurity, cloud computing, big data and analytics, the internet of things (IoT), and simulation. A quantitative study approach, through a survey questionnaire, is utilized to conduct the study. Meanwhile, a sample size of 200 secondary school STEM teachers in Malaysia had been chosen through a simple random sampling technique. Mean scores are used to assess the knowledge. The findings of the study showed that the teachers are highly knowledgeable about the application of simulation (M=4.15), augmented reality (M=4.11), and autonomous robots (M=4.06) in education. Meanwhile, the teachers have moderate knowledge in the application of cybersecurity (M=3.29), additive manufacturing (M=3.27), and the internet of things (M=3.11) in education. Finally, the teachers recorded a low level of knowledge toward the application of cloud computing (M=2.48), horizontal and vertical integration (M=2.20), as well as big data and analytics (M=2.02) in education. This study gives implications for ensuring the teachers can meet the demands of the present education system and help their learners become more proficient in learning. In conclusion, integrating 4IR technology is an important aspect that needs to be utilized in teachers' teaching practices.

This study extends the work of Park and Ellis (2020) with its use of randomized data in Excel® assignments by incorporating a feedback feature that allows students to check their work before submitting it for grading. Three groups of intermediate accounting students complete identical assignments that vary only in the mode of data delivery. One group receives a set of static data so that each student has the same information to complete the assignment. A second group receives data through a randomized number generating function facilitated by an Excel® macro. The third group's data is randomized but the Excel® file also includes a self-check answer function that they can use prior to submitting the assignment for grading. The results indicate that the third group scores the highest and has the greatest satisfaction with the assignment. The study provides evidence that accounting instructors will serve their students better when their Excel® assignments provide randomized data and a self-check answer feedback feature. It also re-enforces the practice of enabling the check answer feature often found in online homework management systems.

Throughout the period of pandemic, many studies have been conducted on emergency remote teaching (ERT) in different fields and from different perspectives, which reveal that there has been a lack of a comprehensive map showing the rapid and continuous responses of these studies to the process. The purpose of this research is to analyze open access research on ERT using bibliometric method, and to reveal current trends in this field. VOSviewer software was used for data analysis; the data collection process was shaped using the PRISMA framework. 238 studies were included in the analysis. The distribution of the open access studies analyzed in the field of emergency remote teaching by year, type of publication, subject, country, and sources was examined; citation analysis (by journal and publication), authorship patterns and collaboration, common word analyses are included. It was found out that these open access publications mainly consisted of journal articles and were dated 2020 and 2021; most of the publications were in the field of educational sciences. Based on common word analysis, the most important topics that are addressed in studies on the ERT process are the process of pandemic, distance education and higher education, while the challenges experienced regarding teachers and pedagogic issues during the process, teacher education, student-related characteristics (such as self-regulated learningmotivation-academic success) and participation are found to be frequently studied topics. The concepts of instructional design, collaborative learning, social presence, and assessment are also among the topics covered. It is anticipated that the implications for policy and practice based on the examination of research trends will have a significant effect on the structuring of future online learning environments, as well as the ERT designed for emergencies.

Cognitive diagnostic models (CDMs) have received much interest within the field of language testing over the last decade due to their great potential to provide diagnostic feedback to all stakeholders and ultimately improve language teaching and learning. A large number of studies have demonstrated the application of CDMs on advanced large-scale English proficiency exams, such as IELTS, TOEFL, MELAB, and ECPE. However, too little attention has been paid to the utility of CDMs on elementary and intermediate high-stakes English exams. The current study aims to diagnose the reading ability of test takers in the B1 Preliminary test, previously known as the Preliminary English Test (PET), using the generalized deterministic input, noisy, "and" gate (G-DINA; de la Torre, 2011) model. The G-DINA is a general and saturated model which allows attributes to combine in both compensatory and non-compensatory relationships and each item to select the best model. To achieve the purpose of the study, an initial Q-matrix based on the theory of reading comprehension and the consensus of content experts was constructed and validated. Item responses of 435 test takers to the reading comprehension section of the PET were analyzed using the "G-DINA" package in R. The results of attribute profiles suggested that lexico-grammatical knowledge is the most difficult attribute, and making an inference is the easiest one.

The Autonomous Vehicles (AV) are a self-driving vehicle capable of sensing its environment and operating with minimal or no human intervention converting into a fully or partially automated vehicle. These automated vehicles have great potential to revolutionize the automotive industry and our daily lives. Thus, they are receiving a lot of attention from automotive industries, governments, suppliers, educational sectors, researchers, and many other stakeholders. According to global financial service firm UBS, the AV market could reach more than $2 trillion by 2030. These projections will become reality if enough skilled workforces are produced with a high level of broad technical competencies specifically for the automotive environment. This will also require consumer education on AVs that could bring all the comfort and excitement across all segments of the population. This brings a substantial opportunity for the educational sectors in generating the workforce required in AV sectors by enhancing the existing curricula in the areas related to AVs or developing a cross-disciplinary course or capstone project that could expose students with a technical background in computer science, computer engineering, electrical engineering, and mechatronics. Since AV has many technologies involved, it is challenging to design an appropriate curriculum and provide hands-on activities. Small-scale learning kits may be an affordable and effective solution for introductory courses on AVs or for beginners. This paper will discuss the use of those widely available small-scale learning kits, their benefits, and their challenges.

Rapid advancements and emergent technologies add an additional layer of complexity to preparing computer science and information technology higher education students for entering the post pandemic job market. Knowing and predicting employers' technical skill needs is essential for shaping curriculum development to address the emergent skill gap. Examining online advertisements to determine the skills sought by employers of new hires for these emerging areas and ensuring that program course content addresses these skills can be a daunting task. In this paper, the authors describe the development of a data-driven analytics framework that can be used for evaluating emerging skill clusters in online job adverts and the application of the framework to a mobile computing course at the authors' institution.

With the incremental integration of technology in writing assessment, technology-generated feedback has found its way to take further steps toward replacing human corrective feedback and rating. Yet, further investigation is deemed necessary regarding its potential use either as a supplement to or replacement for human feedback. This study aims to investigate the effect of blending teacher and automated writing evaluation, as formative assessment feedback, on enhancing the writing performance among Iranian IELTS candidates. In this explanatory mixed-methods research, three groups of Iranian intermediate learners (N=31) completed six IELTS writing tasks during six consecutive weeks and received automated, teacher, and blended (automated + teacher) feedback modes respectively on different components of writing (task response, coherence and cohesion, lexical resource, grammatical range and accuracy). A structured written interview was also conducted to explore learners' perception (attitude, clarity, preference) of the mode of feedback they received. Findings revealed that students who received teacher-only and blended feedback performed better in writing. Also, the blended feedback group outperformed the others regarding "task response," the teacher feedback group in "cohesion and coherence," and the automated feedback group in "lexical resource." The analysis of the interviews revealed that the majority of the learners confirmed the clarity of all feedback modes and learners' attitude about feedback modes was positive although they highly preferred the blended one. The findings suggest new ideas to facilitate learning and assessing writing and support the evidence that teachers can provide comprehensive, accurate, and continuous feedback as a means of formative assessment.

Purpose: The purpose of this study was to investigate the effectiveness of a self-administered naming treatment for one individual, B.N., presenting with semantic variant primary progressive aphasia (svPPA) and a history of traumatic brain injury (TBI). Method: Naming treatment included components of Lexical Retrieval Cascade Treatment and was self-administered using an adaptive spaced retrieval software, Anki. Using a multiple-baseline, single-case experimental design, naming accuracy probes were taken during pretreatment, treatment, posttreatment, and follow-up (through 12 months) for 60 trained words and 10 untrained words. Item-level Bayesian generalized mixed-effects models were used to estimate (a) the treatment effect for trained words, (b) change in untrained words, and (c) maintenance of treatment effects from posttreatment to each subsequent follow-up. Results: Statistical analyses revealed that a gain of 35 out of 60 trained words (35.3; 90% CI [30.6, 39.5]) was directly attributable to treatment. Following treatment, evidence of generalization to untrained words was not observed. During the follow-up period, there was gradual decline in naming accuracy of trained items. Conclusions: The positive treatment results reported here support the use of self-administered naming treatments for those with svPPA and a history of TBI. Although the utility of this treatment approach is constrained by patient factors including motivation, self-administered naming treatments represent a unique opportunity to expand access to speech-language intervention for people with svPPA, including those with concomitant diagnoses.