Internationally accredited engineering programmes are becoming increasingly important in the internationalisation agenda of universities. ABET has highlighted transversal skills in its accreditation criteria for engineering degrees. Preferred transferable skills include the ability of students to reflect on their own performance, the ability to give constructive feedback and the ability to make judgements. Students' self- and peer-assessment was examined in the context of a basic mathematics course. During the maths midterm tests, students self-assessed on each task, and assessed another student's test. These assessments were compared with the points given by the teacher. 84% of students overestimated their actual performance and more than 60% of them overestimated their peer's performance, and both overestimations were low. According to students' opinion, peer assessment is as easy as self-assessment, it is not easier for them to spot mistakes in other people's work than in their own. The research results showed significant difference in the accuracy of peer and self-assessment, peer assessment is closer to teacher evaluation than self-assessment. Contrary to our previous research, now we did not find a significant correlation between students' performance and assessment accuracy in the first test. One reason for this may be that these students have failed this subject at least once. As further learning is only possible once we have identified what needs to be learned, the ability to assess the gained knowledge as accurately as possible is appreciating. In addition to meeting accreditation requirements, the different type of assessments' cognitive and affective effects on learning outcomes make it a good choice for classroom use.

There is a high drop-out rate in engineering higher education, the reasons can be grouped into four categories: economic explanations, individual pedagogical-psychological, learning-related reasons, socio-cultural influences. This paper discusses the problem of the accuracy of self-assessment among the individual pedagogical and psychological reasons. During the maths midterm tests, students self-assessed on each task, which was compared with the points given by the teacher. More than 80% of students overestimated their actual performance in all midterms, and this overestimation was moderate. Based on the relationship between accuracy scores and test results, students who achieved better results in the midterms gave more accurate self-assessments than those who performed poorly, which confirms the Dunning-Kruger effect in engineering education. Feedback based on their performance may affect the accuracy of self-assessment. Feedback from the midterm caused a significant improvement in self-assessment for students who met mid-term requirements, while there was no such improvement for those who did not. Thus, underperforming students did not benefit enough from the feedback from the first midterm, and the accuracy of their self-assessment did not improve. The fact that there is a significant difference in the accuracy of self-assessment between students who fulfilled mid-term requirements and those who did not. The self-assessments of the former was closer to the teacher's evaluation than those of the latter. This may be problematic because weaker students are less aware of their deficiencies due to their inaccurate self-assessment, and thus they may stop their preparation short of what is necessary, and may not ask for help when needed.

Mathematics is a language for expressing physical, chemical and engineering laws nevertheless engineering students often perform poorly in mathematics. Studying can be influenced by several different social, cognitive and non-cognitive factors which all can have an impact on students' academic performance. Many researches revealed positive effects of mathematics self-efficacy on mathematics achievement. Similar results can be found among learning approaches, students using deep-approach achieve better results. It is legitimate to question whether there is a relationship between self-efficacy and learning approaches. My research focused on the interrelationship between two aspects of mathematics self-efficacy (mastery experience, physiological state), learning approaches (deep strategy, deep motive, surface strategy, surface motive) and achievement. This research also examined the variance of self-efficacy, learning approaches and achievement in relation to the number of failed attempts. 306 undergraduate engineering students at a Hungarian university took part in the study. To examine the above mentioned question the study employed quantitative approach and data were collected using two questionnaires during the semester. The self-efficacy scale was adapted from a variety of sources and was modified to local conditions. To measure learning approaches the Revised Two Factor Study Process Questionnaire was rephrased to the domain of mathematics and to the local conditons. The data were analysed quantitatively using descriptive statistics, bivariate correlation, partial correlation, and regression analysis. The results show that self-efficacy, learning approaches and academic achievement were strongly correlated with each other. Students who have higher level self-efficacy use deep strategy in learning and have deep motives, while students classified as low in self-efficacy adopted surface learning approaches. A new variable was introduced which has not been investigated yet in other researches: the number of failed attempts. A significant correlation between the mentioned variables and the number of attempts was identified. My results demonstrate the importance of such kind of learning environment which fosters self-efficacy and deep learning approach.

Students in engineering education need tools to gain insight into the ever-increasing complexity of engineering problems and possible solutions in the 21st century (e.g. seeking the reasons for the recent bridge-collapse in Genova). One of these tools could be the utilization of mathematical knowledge and skills - but many engineering students are undermotivated in studying mathematics. Not only Comenius but our digital age also prefers visualization over textual comprehension, as the Net generation is visually literate. Newer interdisciplinary research findings in brain functions and brain maturation are worth to be integrated into the pedagogy of teaching mathematics to engineers. Methodologically, in order to improve the quality of teaching Mathematics in engineering education at a Hungarian university, both findings in brain-research as well as theories of adult learning have been analysed from the perspective of visualization. The other direction of the work was focused on different types of visualization in Mathematics (according to Guzman), particularly in textbooks for engineering students. Ten textbooks, (among them the newly developed „Mathematics 1” at the Széchenyi István University), available both in print and online in Hungary have been compared from visual aspects. The current Curriculum of the subject „Mathematics 1” has also been analyzed from visual aspects. Findings show the need for a wider variety of visualization. Systematically detailing all of the above-mentioned perspectives and findings of data-processing contribute to developing an up-to-date conceptual framework for improving the quality of teaching Mathematics in engineering education at a Hungarian university, and it might be useful for other universities as well.

Our experience of learning mathematics in schools is often based on learning and applying formulas. This suggests that teaching follows a predominantly deductive approach in which students use predetermined rules and theorems to solve problems. This is the dominant method worldwide, but the question arises: is it possible to teach mathematics in other ways, for example by an inductive approach, which first introduces students to general relationships by starting with concrete examples? The aim of this study was to evaluate the effectiveness of both inductive and deductive learning methods in engineering mathematics education. The experimental group learned the basics of integration through group work using either an inductive or a deductive approach. Their acquired knowledge was assessed through a test, which was repeated one week later. Following the learning process, they also completed a questionnaire in which they described their learning experience, strengths, and challenges. The results indicate that both methods have advantages and challenges with no significant difference in overall effectiveness based on test scores. Students generally reported positive experiences, but some suggested improvements such as restructuring group sizes, providing a mix of both methods, and increasing opportunities for cross-group discussions. Overall, the findings suggest that integrating elements of both learning approaches could offer a more balanced approach, catering to different learning styles while maintaining engagement and conceptual clarity.