This study investigates the utilization pattern, perception, and experience of Higher Education Institutes (HEIs) students towards the ChatGPT application in an academic context. It employs a quantitative approach utilizing a questionnaire as the research instrument. The study sample was selected using a simple random sampling method from Sunway University and Sunway College in Kuala Lumpur, Malaysia. The survey participants, enrolled in General Studies Subjects (MPU) during their short semester between September and December 2023, were selected using a simple random sampling method. Out of 150 students who received the survey via Google Forms, 119 provided complete responses suitable for analysis. The research primarily focused on calculating mean scores to assess three key dimensions: its use patterns of ChatGPT, perceptions and experiences among students towards its adoption in educational contexts. A descriptive analysis was conducted to determine student frequency and percentage values for ChatGPT usage. At the same time, mean scores were utilized to evaluate higher education institutes (HEIs) students' perceptions and experiences with the application in an academic context. This descriptive analysis revealed a spectrum of responses that ranged from low to very high levels across these dimensions. The findings of this study offer extensive insight into the current incorporation and perception of ChatGPT within Higher Education Institutions (HEIs), showcasing the diverse range of engagement and acceptance levels among students.
Choosing a career and educational path is a challenging decision for young people. Career planning conversational agents (CAs) can assist by identifying suitable occupations and educational paths. Trustworthiness is an important dimension for the acceptance of a career planning CA and is influenced by several factors. We conducted a user study with n=114 participants across three schools in Germany to explore the trustworthiness of different career planning CAs. We examined the correlation between trustworthiness and perceived competence, autonomy, and social relatedness from self-determination theory (SDT), as well as the explainability of interactions and several usability dimensions of the assistants. These dimensions included the ability to guide the conversation, onboarding quality, error tolerance, and information relevance. We tested three different variants of the career planning assistant: a form-based assistant, an intent-based CA, and a large language model (LLM)-based CA. The results showed that the LLM-based CA was on average significantly more trustworthy and was perceived as more explainable than the intent-based CA. Key trust factors included conversation flexibility, chatbot credibility, intent recognition, and maintenance of a secure conversation. Additionally, perceived autonomy was crucial for trust across all types of assistants and perceived relatedness for the two CAs. Our findings highlight key areas essential for developing trustworthy CAs.
The online coding tutorial systems offer learners a flexible and accessible platform to acquire coding skills, while educators benefit from the ability to monitor progress, provide personalised feedback, and facilitate effective instruction at scale. However, to develop truly effective and engaging online coding tutorial systems, it is crucial to understand the dual perspectives of both learners and educators. This paper presents learners and educators feedback on an initial list of supportive features that have been identified in a previous study to enhance the quality of designing online coding tutorial systems (OCTSs). An online questionnaire was distributed among 37 learners and educators from Saudi Arabia, the United Kingdom, and other locations to investigate their needs and measure their satisfaction with the list of supportive features that are provided by the current online coding tutorial system called LearnPython. Finally, this study offers some suggestions for supportive features to enhance the quality of designing OCTSs totally based on learners and educators experiences.
The integration of generative AI tools into education has the potential to revolutionize learning experiences, particularly in computer science. This paper explores the adoption and utilization of generative AI tools among computer science students at the University of Applied Sciences Campus Vienna in Austria through a comprehensive survey. The study aims to understand the extent to which AI tools like ChatGPT are integrated into students' academic routines, their perceptions of these tools, and the challenges and opportunities they present. The survey results indicate a high level of acceptance and frequent use of AI tools for tasks such as programming, exam preparation, and generating simplified explanations. However, concerns about the accuracy of AI-generated content and the potential impact on critical thinking skills were also highlighted. The findings underscore the need for clear institutional guidelines and ethical considerations in the use of AI tools in education. This paper contributes to the growing body of literature on AI in education and provides insights for educators and policymakers to enhance the responsible integration of AI technologies in computer science curricula.
The ability to draw syntactic tree diagrams shows students’ knowledge of sentence structures. This study aims to measure the students’ accuracy in drawing tree diagrams using a computer program and their opinions regarding the use of a computer tree generator. The participants were 30 English department students who had been learning English syntax. They were instructed to draw tree diagrams using a program called Syntax Tree Generator. The findings show that 84.56% of students can draw syntactic tree diagrams accurately using the program indicating their good knowledge of English sentence structure. However, they admit that using a computer program to draw tree diagrams is annoying and complicated. The results revealed that the computer tree generator helps produce neat and accurate tree diagrams, but the program should be made more accessible for the learners.
Recent developments in artificial intelligence (AI) have generated discussions around and expectations for its impact in education. In a society where AI plays an increasing role, a basic understanding for the technology and its potential is considered crucial. Still, many educators feel apprehensive towards the use of AI in education, which naturally affects students’ opportunities to learn about and use AI-supported solutions. Users’ preference and trust in relation to these tools also becomes important when integrated in education. Bringing light on students’ perceptions of AI might help educators to better understand the value of such applications. The present study aims to provide insight into how university students perceive AI in general and, more particularly, the idea of having a machine performing tasks that have traditionally been handled by a teacher or a teaching assistant. The results are based on 140 Swedish and Taiwanese university students’ responses to an online questionnaire. Our results indicate that the students have quite positive perceptions of AI: they might still not feel very competent in AI, but show a large interest in the topic and are positive about its consequences for society. Nevertheless they also see potential drawbacks of the technology. As a whole, our findings indicate that Swedish and female students tend to prefer human interactions over AI-supported tools for learning.
One of the most significant challenges facing higher education institutions is the continual improvement of teaching quality. This is particularly evident in Brazil, where despite the growing number of students enrolled in undergraduate courses, a high dropout rate persists. Among the various factors contributing to this phenomenon, the quality of the courses plays a pivotal role. In light of the aforementioned considerations, this study presents the QualiAcad tool, which performs data mining through web scraping of the Guia da Faculdade survey. This survey evaluates higher education courses in Brazil, employing the following criteria: (1) Pedagogical Project; (2) Faculty; and (3) Infrastructure. QualiAcad is utilized to scrape data from this research and generate dashboards to analyze the evaluation of higher education courses from a large educational group in Brazil and its competitors. The scraping process was conducted over a five-year period, providing educational group managers with a historical and analytical view of the quality of their courses. This allows for the formulation of plans to contain evasion and enables historical comparisons with competitors to determine more effective competitive strategies. This approach not only improves analyses but also provides robust insights to guide better-informed future decisions.
This paper describes a model designed to detect miscues of children's oral reading. The model is evaluated on a corpus of primary school children with and without reading difficulties in Dutch. This set contains real words and pseudo words reading tests. The automatic speech recognition task is achieved by training an End-to-End (E2E) model with phonemic targets. The encoder employs the Conformer architecture, and the decoder follows the Transformer decoder scheme. The automatic assessment relies on modeling a lexicon at a phonetic level using a Weighted Finite State Transducer (WFST) that models the pronunciation lexicon. The proposed WFST construction accommodates all the pronunciations defined by the lexicon for any given word, allowing the assessment to handle multiple pronunciations. The experiments show that the accuracy of this setup outperforms previous results obtained on the same evaluation set.
This paper explores the application of collaborative learning in the digital preservation of intangible cultural heritage. It focuses on how modern educational technologies can protect and transmit intangible cultural heritage through innovative practical courses for college students. Conducted over four years (2021-2024), the study employs a “four-dimensional collaborative learning model” that integrates inquiry-based learning, scaffolded instruction, situated teaching, and cooperative learning. This model promotes in-depth student learning and practical application. The data include survey questionnaires and interview materials. Results indicate that collaborative learning enhances students' understanding and interest in intangible cultural heritage and improves their ability to solve real-world problems using modern technologies. The study evaluates the effectiveness of this approach in educational practice and its potential for protecting intangible cultural heritage.
The course on programming methodology is integral to computer science and software engineering education. Knowledge graph has been an interesting topic in recent decades, and knowledge graph has propelled its use in systematically organizing, thoroughly analyzing, and fully leveraging knowledge to become a focal point in teaching research and application, yielding significant progress. Therefore, this paper proposes a method for constructing a knowledge graph tailored to the curriculum. Furthermore, it establishes a comprehensive course knowledge graph, a student capability knowledge graph, and a course resource knowledge graph. Upon this foundation, by leveraging the multi-model database ArangoDB and the visualization framework GraphVIS, a visualized system for a curriculum knowledge graph has been realized. This study aims to offer a reference for the construction and pedagogical approaches of the course, and preliminary applications of the system have received positive feedback.
Collaboratively and iteratively evolving Open Educational Resources (OER) can lead to high-quality material. Increased reuse of such OERs justifies the required effort of creating them. However, current approaches to composing, adapting and sharing OERs often are one-way-streets where improvements are not fed back into the original resource. We argue that that there are two main reasons for this: (i) the granularity of the resources is too coarse and (ii) the resources are not linked but copied. This paper presents data that shows how programming exercises are reused and improved over time. The data is collected from a system used in a closed setting at one university. We analyze the data and derive requirements for a system that can be applied in a broader OER context. Based on the requirements, we propose a system that allows for the dynamic linking of modular components in a broader OER context. We will use the system for future real-world use cases where we plan to analyze potential conflicts between linking and adapting resources.
Prompt and sufficient feedback is essential for students' academic learning since it enables them to review their learning techniques and improve their areas of weakness. Nevertheless, delivering personalised feedback to every student continues to be difficult for teachers due to its demanding and time-intensive nature. While automated feedback systems are available, their primary focus is providing feedback on a single subject, and most of them utilise statistical analysis or traditional machine learning techniques to provide feedback. Moreover, no feedback model utilises the same criteria to generate text-based feedback for more than one subject. Generative artificial intelligence (GEN AI) has recently made incredible progress, and large language models (LLMs) can retain the context from the vast amount of text. Hence, this research presents a framework that employs an innovative technique to offer text-based feedback to students in different fields of study. This framework employs two LLMs, one for generating the feedback and another for categorising it into separate subjects using suitable headings for structural organising. Consequently, the output produced by this technology corresponds to the original tone of the teacher.
Computer Programming (CP) is difficult to teach and learn. Many students who enroll in CP courses have difficulty learning to program, as they have deficits in problem-solving skills and are unable to understand abstract programming concepts. Some studies also state that teaching methods are often not the most appropriate as they are not personalized to each student’s needs and do not complement their learning styles. We believe that Computational Thinking (CT) can help alleviate the difficulties in learning to program, especially if it is developed from an early age. However, it is also crucial that teachers use effective methods to help their students develop CT skills. There are several Learning Resources (LR) (digital or paper format) to develop CT skills, however, we argue that Neuroeducation can contribute to improving the effectiveness of these LR in the teaching and learning process. The evidence about the brain’s learning process can be transformed into principles applicable in Education (Neuroeducation). To understand how we could train CT at various levels of education we built an ontology, OntoCnE, that describes the CT and CP domains. OntoCnE is composed of 3 layers that define: how to develop CT; which concepts should be taught at each level of education; and appropriate training materials. This paper aims to present a CT development approach based on this ontology together with Neuroeducation guidelines. The result of our research is the fourth layer for OntoCnE, which defines the Neuroeducation guidelines to characterize the materials. To illustrate our proposal, we will use LR PathIt as a case study. This approach can contribute to innovative and more efficient training of CT.
The use of images to enhance recall and expand understanding of various topics by students in all forms of education is important. In this digital age, the use of technology in the classrooms is becoming rampant and inevitable. Therefore, most of the teaching and learning materials or resources including images are presented in the digital format. It is therefore necessary to know what a quality image in education is from literature and also get a database to aid in determining the quality of educational images. It is noted from literature that the concept of image quality is not the same for all authors particularly based on their interest in the field of imagery. In the field of educational technology we have identified a knowledge and theoretical gap in literature since there is little no scientific literature with direct relation to what a quality image used for teaching should be like when in the digital format and can also help enhance easy memory call. Most of the quality assessments of images were done by adults but in education, children also benefit from the images they are shown, so it is necessary to know the images that children perceive as quality using the mean opinion score of the subjective approach and what images are actually of quality by doing an image memory test. It may be necessary to repeat the quality assessment for the three levels of education (basic, secondary and tertiary) since students at each of these levels have a unique set of mental capacity and capability. Also, the various databases that have been developed and discussed in literature do not make use of the five (5) types of educational images and hence cannot be used to make a quality image assessment on various educational images used in the classrooms. We identify that the grouping of all educational images together as one and performing same image quality assessment on them to be a theoretical gap in literature that there is the need to fill. The researchers will fill this theoretical gap by creating groups of images for each of the five different categories of educational images and performing a quality assessment on the images within each individual group. This will enhance the results of the quality assessment of educational images since each group will have a specific result suited for its images. There was also a gap identified in the methodology with regards to the population and sample. The population was mainly not pupils but were adult students and their scores were mainly not based on memory retention which is a main reason for using images in education but based on other factors like aesthetics. It is for this reason this that this study seeks to use a pupil population specifically to determine the perceived quality of educational images that when used in the teaching and learning process will be remembered with ease. A database like this when developed will help teachers to select the most appropriate images to actually use in their lesson deliveries so as to enhance easy recall of the images shown in the classroom to the pupils.
The online collaborative learning platform can increase learners’ class engagement at different levels of education. However, the literature review of this study found that limited studies investigated the impact of using this kind of platform on class engagement among technical college students though they are future skilled talents. Therefore, based on Gunuc and Kuzus's theoretical framework of student engagement, including cognitive, emotional, and behavioral engagement, the first phase of this research project employed quantitative investigation on 71 Chinese technical college students. 36 of them engaged in the intervention learning with online collaborative platforms while the rest of them learned in a traditional way. This paper reported the second and qualitative phase of the entire mixed methods research project, which aimed to have a deeper investigation into how the use of the online collaborative learning platform might affect technical college students’ class engagement. Six students and one teacher from the experimental group in the previous phase of research were purposively selected for semi-structured individual interviews, and the data were analyzed by thematic analysis via NVivo. This study found that the online collaborative learning platform positively influenced technical college students’ engagement in three key ways: (1) affording learners more opportunities for interaction; (2) fostering their sense of enjoyment and motivation for excellence in teamwork; and (3) encouraging them to understand the value of learning and making efforts in various situations.
Artificial Intelligence (AI) is a transformative force in the 21st century, offering significant potential to revolutionize various sectors, including education. However, the disparity in AI literacy between developed and developing nations poses significant challenges to achieving equitable global progress. This review paper aims to address these challenges by proposing a comprehensive AI literacy framework tailored for developing nations and outlining strategic implementation plans. The paper begins by defining AI literacy and emphasizing its critical importance for individuals and societies, particularly in the context of developing nations. It provides an in-depth analysis of the current state of AI literacy in these regions, identifying key barriers such as limited infrastructure, insufficient educational resources, and policy gaps. A detailed AI literacy framework is proposed, focusing on essential competencies and skills, curriculum integration, and the pivotal role of educator training. Strategies for effective implementation are explored, including policy recommendations for governments and educational institutions, the role of public-private partnerships, and the utilization of online educational platforms to enhance accessibility and inclusivity. The review highlights successful case studies from various developing nations, extracting best practices and lessons that can be adapted and replicated. Methods for assessing the impact of AI literacy programs are discussed, emphasizing the long-term benefits for both individuals and the broader society. Finally, the paper considers future directions and innovations in AI literacy, suggesting ways to improve and adapt the framework to meet evolving needs continuously. By thoroughly reviewing the existing literature and offering actionable strategies, this paper aims to contribute to the global effort to promote AI literacy and ensure that developing nations are not left behind in the AI-driven future.
This paper presents the development of an AI-based system to assist visually impaired individuals in navigating safely. The system utilizes a wearable camera and advanced AI algorithms to identify and map "walkable areas" based on the presence of sighted pedestrians. This approach leverages human spatial awareness, providing a robust, real-time solution for navigating complex environments. Unlike conventional methods relying on obstacle detection or pre-defined maps, this system dynamically adapts to changing surroundings. It employs a pre-trained YOLO model for pedestrian recognition and an OAK-D Pro AI camera for real-time pedestrian detection and tracking. The system then generates a walkable area map based on the identified pedestrian movements displayed on a smartphone or tablet. This research addresses the limitations of traditional assistive navigation systems by leveraging pedestrian spatial awareness. The developed system empowers visually impaired individuals with greater independence and confidence in their daily lives by harnessing the power of AI and human spatial perception.
This study critically addresses the imperative to foster meaningful connections and facilitate collaborative learning experiences in diverse global student communities. We present a specialized Blackboard plugin, employing the Scrum methodology and incremental delivery, to surmount geographical barriers and empower students worldwide. Released in three phases, the software integrates personalized profiles, user-friendly search tools, vibrant discussion forums, and gamification elements in Phase 1. Phase 2 further enriches the platform with an event calendar, multilingual support, and robust reporting features. The comprehensive solution is realized in Phase 3, incorporating an AI tool for content monitoring, feedback implementation, and access to training resources. Rigorous testing and positive student feedback substantiate its outstanding performance, marking a significant stride in global education. Beyond enhancing the educational landscape, this collaborative software signifies the potential for transformative global collaborative learning experiences. Its multifaceted features not only bridge geographical divides but also contribute to shaping a future where collaborative learning thrives on a global scale.
Digital teaching has become one of the key approaches in the wave of digital transformation, considering teachers are the key role in digital teaching to achieve educational equity. What is the competence of teachers to cope with digital teaching has rarely been studied, especially with regard to the gender, regional and area differences. To address this gap, this study paid attention to the differences in digital teaching competence of teachers in different gender, regions and areas. A large-scale survey was conducted and 3,732 valid responses were collected (24.2% from rural schools, 31.4% from town schools, and 44.5% from urban schools) among four major regions. The results show that the overall digital teaching competence of teachers is at an intermediate level, with the emergence of the characteristic of “transforming teaching into learning”, but the competence of student-centered learning design, implementation and evaluation needs to be strengthened. What's more, there are significant differences in digital teaching competence among teachers of different genders, regions, and fields, highlighting the digital divide in education among different groups of teachers. Thus, this study provides valuable evidence that can be used to develop flexible, appropriate, and beneficial teacher professional development programs to prepare teachers for the adoption of generative AI in future education.
In order to solve the current shortcomings in the teaching of VR-integrated art majors, the creator teaching method is to apply virtual reality technology to the design and practical teaching of art courses. The innovation of this study is reflected in three aspects. Firstly, virtual reality technology is organically combined with traditional ancient architecture in experimental teaching and applied to art teaching courses. In this way, the subjective initiative and leading role of students in teaching can be given full play. This method can play a positive role in promoting students' practical ability and innovation ability. Secondly, the model approach of VR simulation experiments to fully demonstrate traditional architecture is proposed. This paper discusses how to successfully embed art visuals and place VR at the forefront of a progressive curriculum. This study conducted in a Chinese context used Fine Art students (62) as respondents to apply VR technology to their subject area. Part of the raw data was collected by completing a questionnaire, followed by a visual regression (rs = 2.67, p < .0001). The basic data analysis showed that VR allows students to absorb information quickly compared to the usual teaching methods. This method can play an effective role in scientifically evaluating the effectiveness of teaching and targeting improvements. In conclusion, the digital humanities creator pedagogy has been fully applied in the design and practice teaching of art students.
