A Predictive Model using Machine Learning Algorithm in Identifying Student's Probability on Passing Semestral Course

Purpose: The used of an integrated academic information system in higher education has been proven in improving quality education which results to generates enormous data that can be used to discover new knowledge through data mining concepts, techniques, and machine learning algorithm. This study aims to determine a predictive model to learn students' probability to pass their courses taken at the earliest stage of the semester. Method: To successfully discover a good predictive model with high acceptability, accurate, and precision rate which delivers a useful outcome for decision making in education systems, in improving the processes of conveying knowledge and uplifting student's academic performance, the proponent applies and strictly followed the CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology. This study employs classification for data mining techniques, and decision tree for algorithm. Results: With the utilization of the newly discovered predictive model, the prediction of students' probabilities to pass the current courses they take gives 0.7619 accuracy, 0.8333 precision, 0.8823 recall, and 0.8571 f1 score, which shows that the model used in the prediction is reliable, accurate, and recommendable. Conclusion: Considering the indicators and the results, it can be noted that the prediction model used in this study is highly acceptable. The data mining techniques provides effective and efficient innovative tools in analyzing and predicting student performances. The model used in this study will greatly affect the way educators understand and identify the weakness of their students in the class, the way they improved the effectiveness of their learning processes gearing to their students, bring down academic failure rates, and help institution administrators modify their learning system outcomes. Recommendations: Full automation of prediction results accessible by the students, faculty, and institution administrators for fast management decision making should take place. Further study for the inclusion of some student`s demographic information, vast amount of data within the dataset, automated and manual process of predictive criteria indicators where the students can regulate to which criteria, they must improve more for them to pass their courses taken at the end of the semester as early as midterm period are highly needed

How do we model learning at scale?:A systematic review of research on MOOCs

Despite a surge of empirical work on student participation in online learning environments, the causal links between the learning-related factors and processes with the desired learning outcomes remain unexplored. This study presents a systematic literature review of approaches to model learning in Massive Open Online Courses offering an analysis of learning-related constructs used in the prediction and measurement of student engagement and learning outcome. Based on our literature review, we identify current gaps in the research, including a lack of solid frameworks to explain learning in open online setting. Finally, we put forward a novel framework suitable for open online contexts based on a well-established model of student engagement. Our model is intended to guide future work studying the association between contextual factors (i.e., demographic, classroom, and individual needs), student engagement (i.e., academic, behavioral, cognitive, and affective engagement metrics), and learning outcomes (i.e., academic, social, and affective). The proposed model affords further interstudy comparisons as well as comparative studies with more traditional education models. </jats:p

Foundations of Scholarly Writing

Academic research, transcending disciplinary boundaries, mandates adherence to established core principles, ensuring ethical rigor and substantial contributions to knowledge. Comprehensive literature reviews are pivotal in unearthing research gaps, laying the groundwork for investigations that yield novel insights. The alignment of research questions with robust methodologies—be it qualitative, quantitative, or a hybrid—fortifies the validity of outcomes. Meticulous writing and strategic publication approaches are instrumental in effective dissemination and impact generation. A commitment to transparency, ethical integrity, and the acknowledgment of inherent limitations is fundamental to maintaining credibility. In an era where research is continually evolving, particularly with the advent of new technologies, the agility in ethical reasoning is essential to navigate emerging complexities and ethical quandaries. Efforts to minimize potential harms, honor diverse perspectives, and uphold moral principles remain non-negotiable ethical imperatives. Institutional training programs, established protocols, codes of conduct, and vigilant oversight embed a culture of integrity within academic spheres. Recommendations for future research writing include the development of interdisciplinary teams, ongoing training in critical thinking, and ensuring that ethical standards keep pace with the rapid evolution of technology

From Gatekeeping to Engagement: A Multicontextual, Mixed Method Study of Student Academic Engagement in Introductory STEM Courses.

The lack of academic engagement in introductory science courses is considered by some to be a primary reason why students switch out of science majors. This study employed a sequential, explanatory mixed methods approach to provide a richer understanding of the relationship between student engagement and introductory science instruction. Quantitative survey data were drawn from 2,873 students within 73 introductory science, technology, engineering, and mathematics (STEM) courses across 15 colleges and universities, and qualitative data were collected from 41 student focus groups at eight of these institutions. The findings indicate that students tended to be more engaged in courses where the instructor consistently signaled an openness to student questions and recognizes her/his role in helping students succeed. Likewise, students who reported feeling comfortable asking questions in class, seeking out tutoring, attending supplemental instruction sessions, and collaborating with other students in the course were also more likely to be engaged. Instructional implications for improving students' levels of academic engagement are discussed

A data driven approach to student retention: the impact on leadership behaviour

Student retention is important to all Higher Education Institutions (HEI’s). The typical focus has seen institutions identifying ‘at risk’ students by monitoring a set of factors such as student attendance, engagement, performance, socio-economic background, etc. Institutions want to identify ‘at risk’ students and intervene before the ‘at risk’ student becomes a retention statistic. Once the factors are identified, this typical model often provides data to decision makers (leaders and/or senior managers) to assist with the identification of ‘at risk’ students in each leader’s department. However, some HEI’s have also historically relied on more tacit knowledge (opinions, anecdotes and biases) rather than actual data. In a data driven culture, leaders make decisions based on data and information rather than intuition and bias. HEI’s typically provide relevant data to leaders creating an opportunity to craft an intervention to change student behaviour. Interestingly, whether HEI’s are using data or tacit knowledge, all typically employ the same next steps once an ‘at risk’ student is identified: intervene to try and change the ‘at risk’ student’s behaviour. These interventions are quite consistent across HEI’s and can include supports such as interaction with faculty, mentoring, career guidance, counselling, orientation programmes or even access to technology. These interventions, or supports, can be grouped into three categories: Academic, Environmental and Institutional. What is also interesting however, is that there are a number of methodological and theoretical gaps in the area of student retention research. The vast majority of the research has used positivist approaches to collect and analyse data and focused, understandably, on the perspective of the student. Exploiting these gaps, this exploratory study is building theory by analyzing data gathered through interviews, surveys and participant observation in a HEI. A single case study design is chosen with an Irish HEI as the case. Another crucial difference is that this research focuses on the perspective of the leader rather than the student. After moving towards a data driven culture, the paper will ask a number of key questions: 1. What characterises leadership behaviour in a typical* student retention model? 2. What is the impact of a data-driven approach on leadership behaviour in a student retention model? *A typical student retention model is one which may rely heavily on opinions, biases and anecdotes i.e. (non data-driven). It also focuses on 1st year full time students, which is also the primary focus of this research