First-Year Engineering Students’ Strategies for Taking Exams

Student drop-out is one of the most critical issues that higher educational institutions face nowadays. The problem is significant for first-year students. These freshmen are especially at risk of failing due to the transition from different educational settings at high school. Thanks to the massive boom of Information and Communication Technologies, universities have started to collect a vast amount of study- and student-related data. Teachers can use the collected information to support students at risk of failing their studies. At the Faculty of Mechanical Engineering, Czech Technical University in Prague, the situation is no different, and first-year students are a vulnerable group similar to other institutions. The most critical part of the first year is the first exam period. One of the essential skills the student needs to develop is planning for exams. The presented research aims to explore the exam-taking patterns of first-year students. Data of 361 first-year students have been analysed and used to construct “layered” Markov chain probabilistic graphs. The graphs have revealed interesting behavioural patterns within the groups of successful and unsuccessful students.Peer Reviewe

Education Research Using Data Mining and Machine Learning with Computer Science Undergraduates

In recent decades, we are witness to an explosion of technology use and integration of everyday life. The engine of technology application in every aspect of life is Computer Science (CS). Appropriate CS education to fulfill the demand from the workforce for graduates is a broad and challenging problem facing many universities. Research into this ‘supply–chain’ problem is a central focus of CS education research. As of late, Educational Data Mining (EDM) emerges as an area connecting CS education research with the goal to help students stay in their program, improve performance in their program, and graduate with a degree. We contribute to this work with several research studies and future work focusing on CS undergraduate students relating to their program success and course performance analyzed through the lens of data mining. We perform research into student success predictors beyond diversity and gender. We examine student behaviors in course load and completion. We study workforce readiness with creation of a new teaching strategy, its deployment in the classroom, and the analysis shows us relevant Software Engineering (SE) topics for computing jobs. We look at cognitive learning in the beginning CS course its relations to course performance. We use decision trees in machine learning algorithms to predict student success or failure of CS core courses using performance and semester span of core curriculum. These research areas refine pathways for CS course sequencing to improve retention, reduce time-to–graduation, and increase success in the work field

Toward a model of academic competencies to enhance first-year student retention in higher education

Enhancing student retention in higher education has been a global issue for many years; however, withdrawals prior to degree completion remain at about 30% in member countries of the Organisation for Economic Cooperation and Development (OECD). As early withdrawals have consequences on different levels, such as for the individual, higher education institutions, and society, further research on student retention is still important and necessary. In Germany, empirical evidence describing student retention remains rare. Recently, however, some government initiatives on student retention in Germany have been funded, such as with regard to the demographic change and the demand for qualified academic employees for the German economy. The first year of higher education is considered particularly crucial, as students often decide to withdraw at this stage. Important factors for discontinuing higher education include the choice of the wrong course, lack of motivation, overload, an unsatisfactory first-year experience, lack of institutional support services, and personal factors, such as financial problems, health, and family circumstances. First-year students’ academic unpreparedness, perceptions, and expectations have also been found to contribute to discontinuing higher education studies. First-year students’ academic preparedness for higher education studies, which can be linked to the concept of generic skills, has not been thoroughly examined in the extant literature. Therefore, in the presented thesis, a conceptual model of decisive academic competencies for higher education was constructed, following a competency-based approach and with a focus on the first year in higher education. This model is designed to complement established models and theories addressing student retention in higher education, with a focus on five academic competencies: time management, learning skills, self-monitoring, technology proficiency, and research skills. This thesis aims to provide insight into students’ (student perspective) and academic staff (academic staff perspective) expectations and perceptions concerning academic competencies for higher education studies as well as the potential of learning analytics and digital badges (educational technology perspective). The thesis includes three quantitative studies (Study 1, Study 2, Study 3), one qualitative study (Study 4), and one theoretical research effort (Integrative review) to enhance first-year student retention in higher education and contribute to research in Germany. The proposed model of academic competencies may address the research gap relating to generic skills in higher education studies and serve as a platform for discussion about required academic competencies for higher education studies. Regarding the student perspective (Study 1, Study 2), one main finding indicated that first-year students assessed their skill levels in all five academic competencies as rather high. The findings also indicated first-year students’ perceptions of the role of academic staff in supporting student development, especially in research skills, and low self-reported confidence in this competency. Study 3 indicates that first-year students’ intention to leave the institution prior to degree completion may be influenced by their perceptions and expectations with regard to academic competencies, especially research skills. Regarding the academic staff perspective (Study 4), interviews with members of the academic staff indicated that their perceptions of first-year students’ academic competencies are lower than staff expectations. Academic staff often expect first-year students either to have already developed competencies for higher education studies based on their prior secondary education or to be responsible for developing these competencies on their own. Regarding the educational technology perspective (Integrative review), first learning analytics and then digital badges were presented, with an explanation of their objectives, purposes, functions, opportunities, and challenges concerning their potential to enhance student retention in higher education. Lastly, a conceptual model was proposed that synthesizes learning analytics, digital badges, and academic competencies. The findings of this thesis are discussed, practical implications are derived, and ideas for future research are presented. The findings in this thesis may contribute to the development of adequate support services that meet individual needs and move research on the first-year experience and academic competencies forward to enhance student retention in higher education