No abstract

Students in undergraduate chemistry courses find, as a rule, topics with a strong mathematical basis difficult to master. In this study we investigate whether such mathematically related problems are due to deficiencies in their mathematics foundation or due to the complexity introduced by transfer of mathematics to a new scientific domain. In the investigation we exposed a group of students to a chemistry instrument based on the Nernst equation in electrochemistry, and an equivalent group of students to a similar mathematics instrument in which the questions were stripped of all chemistry context. Both tests contained items requiring algebraic as well as graphical skills. Students experienced few problems with the algebraic questions in both the chemistry and mathematics tests. Their graphical construction and interpretation skills, on the other hand, are inadequate, as can be seen from the poor performance in both the mathematics and the chemistry results of the graphical question. Our conclusion is that the problem seems to lie at the mathematics side and is not due to the transfer of mathematics to an application. © Wiley Periodicals, Inc. J Res Sci Teach 45: 197–218, 2008.

No abstract

Higher education faces the challenge of high student attrition, which is especially disconcerting if associated with low participation rates, as is the case in South Africa. Recently, the use of learning analytics has increased, enabling institutions to make data-informed decisions to improve teaching, learning, and student success. Most of the literature thus far has focused on “at-risk” students. The aim of this paper is twofold: to use learning analytics to define a different group of students, termed the “murky middle” (MM), early enough in the academic year to provide scope for targeted interventions; and to describe the learning strategies of successful students to guide the design of interventions aimed at improving the prospects of success for all students, especially those of the MM. We found that it was possible to identify the MM using demographic data that are available at the start of the academic year. The students in the subgroup were cleanly defined by their grade 12 results for physical sciences. We were also able to describe the learning strategies that are associated with success in first-year biology. This information is useful for curricular design, classroom practice, and student advising and should be incorporated in professional development programs for lecturers and student advisors.

Early identification of students at risk of failing first-year chemistry allows timely intervention. Cognitive factors alone are insufficient predictors for success; however, non-cognitive factors are usually difficult to measure. We have explored the use of demographic and performance variables, as well as the accuracy of self-evaluation as an indicator of metacognitive ability, as possible indicators for students at risk of failing the first semester course in General Chemistry (CMY 117) at the University of Pretoria. Variables with a strong correlation with performance in CMY 117 were used to develop a prediction model based on logistic regression. Three variables, i.e. prior performance in mathematics and in physical science, and the extent of overconfidence expressed as the ratio between expected and actual performance in a chemistry pre-test written at the start of the semester, were shown to be significant predictors for risk of failing. The highest overall accuracy of prediction (76%) was obtained for a subset of students with a C or D grade for their high school leaving examination in mathematics when high risk students were defined as those with a final mark for CMY 117 as 51% or lower. The prediction model, based on the model building data set, had a sensitivity of 92% and a specificity of 46%; whilst the sensitivity and specificity using the validation data set were 88% and 38% respectively.