The efficacy of using data mining techniques in predicting academic performance of architecture students.

In recent years, there has been a tremendous increase in the number of applicants seeking placement in the undergraduate architecture programme. It is important to identify new intakes who possess the capability to succeed during the selection phase of admission at universities. Admission variable (i.e. prior academic achievement) is one of the most important criteria considered during selection process. The present study investigates the efficacy of using data mining techniques to predict academic performance of architecture student based on information contained in prior academic achievement. The input variables, i.e. prior academic achievement, were extracted from students' academic records. Logistic regression and support vector machine (SVM) are the data mining techniques adopted in this study. The collected data was divided into two parts. The first part was used for training the model, while the other part was used to evaluate the predictive accuracy of the developed models. The results revealed that SVM model outperformed the logistic regression model in terms of accuracy. Taken together, it is evident that prior academic achievement are good predictors of academic performance of architecture students. Although the factors affecting academic performance of students are numerous, the present study focuses on the effect of prior academic achievement on academic performance of architecture students. The developed SVM model can be used a decision-making tool for selecting new intakes into the architecture program at Nigerian universities

The Relationship of Perceived Learning and Self-Regulated Learning of Undergraduate Students and the Curiosity Scores Generated by Packback

Institutions work to improve their retention rates. Research supports academically and socially integrated students are more likely to develop a commitment to the institution and persist to graduation. Historically these theories emphasized perceived learning and self-regulated learning as contributing factors for student retention. Curiosity is a motivational factor that improves student engagement and academic integration. Discussion boards are used with face-to-face, online, and hybrid courses. Instructors use the virtual workspace to build a collaborative community for students to engage with one another, the instructor, and the course material. Packback uses artificial intelligence (AI) to heighten student engagement on discussion board posts by providing immediate feedback to students and publishing a leader board with curiosity scores. Through the lens of Connectivism and the Community of Inquiry Model for online learning, this predictive correlational study explored the relationship of perceived learning and self-regulated learning of students enrolled in an undergraduate political science course and the curiosity score generated by Packback. The study involved a convenience sample from a land grant institution located in the southeastern United States . The Cognitive, Affective, and Psychomotor (CAP) survey measured perceived learning using a seven-point Likert scale. The Online Self-Regulated Learning Questionnaire (OSLQ) measured self-regulated learning behaviors using a five-point Likert scale. Packback’s Curiosity Score is generated through an algorithm using presentation, credibility, and effort. A multiple regression analysis demonstrated a lack of sufficient evidence to support a predictive relationship between perceived learning and self-regulated learning (predictor variables) upon curiosity scores (criterion variable) generated by Packback

Determinants of learning management systems adoption in Nigeria: A hybrid SEM and artificial neural network approach

Research has shown that technology, when used prudently, has the potential to improve instruction and learning both in and out of the classroom. Only a handful of African tertiary institutions have fully deployed learning management systems (LMS) and the literature is devoid of research examining the factors that foster the adoption of LMS. To fill this void, the present research investigates the factors contributing to students’ acceptance of LMS. Survey data were obtained from registered students in four Nigerian universities (n = 1116); the responses were analyzed using artificial neural network (ANN) and structural equation modeling (SEM) techniques. The results show that social influence, facilitating conditions, system quality, perceived ease of use, and perceived usefulness are important predictors for students’ behavioral intention to use LMS. Students’ behavioral intention to use LMS also functions as a predictor for actual usage of LMS. Implications for practice and theory are discussed.No sponso

The impact of emotions on student participation in an assessed, online, collaborative activity

There is growing recognition of the importance of emotions in academic online learning contexts. However, there is still little known about the role of emotions in social and collaborative online learning settings, especially the relationship between emotions and student participation. To explore this relationship, this study used a prospective longitudinal research design to follow 46 distance learning students throughout a 3-week assessed, online, collaborative activity. This approach allowed the fluctuating and dynamic aspects of emotions to be explored as well as the relationship between emotions and student participation in the collaborative activity. Self-report data were gathered using a semistructured online diary at five time points throughout the task (once at the start of the collaborative activity, three times during the activity, and the final entry after the activity had finished). Findings revealed that learners generally perceived pleasant emotions (such as relief, satisfaction and enjoyment) to have positive impacts, or no impact, on participation, whereas unpleasant emotions (such as anxiety, frustration, and disappointment) were generally perceived to have negative impacts, or no impact, on participation. Interestingly, however, anxiety, and to a smaller extent frustration, were perceived by a number of students to have positive impacts during the activity. To conclude this paper, implications for educators are highlighted

ChatGPT -- a Blessing or a Curse for Undergraduate Computer Science Students and Instructors?

ChatGPT is an AI language model developed by OpenAI that can understand and generate human-like text. It can be used for a variety of use cases such as language generation, question answering, text summarization, chatbot development, language translation, sentiment analysis, content creation, personalization, text completion, and storytelling. While ChatGPT has garnered significant positive attention, it has also generated a sense of apprehension and uncertainty in academic circles. There is concern that students may leverage ChatGPT to complete take-home assignments and exams and obtain favorable grades without genuinely acquiring knowledge. This paper adopts a quantitative approach to demonstrate ChatGPT's high degree of unreliability in answering a diverse range of questions pertaining to topics in undergraduate computer science. Our analysis shows that students may risk self-sabotage by blindly depending on ChatGPT to complete assignments and exams. We build upon this analysis to provide constructive recommendations to both students and instructors.Comment: This is a work in progres

The impacts of artificial intelligence on management accounting students:a case study at Oulu Business School, University of Oulu

Abstract. The pervasiveness of Artificial intelligence in accounting fraternity has come under serious scrutiny. Artificial intelligence is defined as the intelligence that machines exhibit by imitating human behaviour. AI is altering the roles of accountants. It is redefining the job descriptions of all sectors of professions of which management accountants is not immune to in the global world. There are two begging questions that this study seeks to investigate One concern with the measures put in place by faculty in impacting students with knowledge and skills in AI. The second question concerns the willingness and readiness of management accounting students to adopt the skills and knowledge of AI. The aim of the study is to investigate the impacts of AI on management accounting students. An exploratory design was used to examine the impacts of AI on management accounting students at the Oulu Business School (OBS), University of Oulu. Data was collected through an open-ended interview. Purposive sampling was used to identify eight expertise of whom three were academic professors in accounting, and five were second-year master’s degree students from management accounting discipline. Following a qualitative method approach, the investigation involves recording and transcription of the recorded interviews coupled with traditional notes taking which was later coded, categorized, patterned and themes identified. A semi-structured questionnaire was used to obtain the needed information. The study established from the interview reveals that there is an integration of AI in accounting curriculum, there are good textbooks that integrate AI into accounting but insufficient textbooks that combine AI and management accounting, there is student’s awareness of AI through a seminar, insufficient expertise to teach AI. Findings from students reveal that students are aware of AI; students are also willing to adopt and learn AI skills. Only a few students are oblivious about adopting the skills and knowledge of AI. Majority of the students have also taken AI related courses in their undergraduate studies. Also, the finding indicated that most students have acquired the basic skills and knowledge in database management. Recommendation for the researcher is that management accounting students must continually improve the knowledge and skills in AI to be a ready market in the global world of work and stay relevance now and the future