There has been a great deal of effort over the years in developing instruments to predict student success and reduce failure rates in general chemistry. While the results often include placement examinations at the post-secondary level (e.g., Scholastic Aptitude Test (SAT), Toledo Chemistry Placement Examination (TCPE), California Chemistry Diagnostic Test (CCDT)), they have also generated tools and strategies to identify students at risk, which is also applicable to learners at the secondary level. Predictors can also include using previous school grades, Piagetian tasks, and Test of Logical Thinking (TOLT). In addition, non-cognitive student attributes, such as background, possessing a student loan, attitude to science, motivation, self-efficacy and general demographics, have been used in other studies to predict course withdrawals and eventual grades.
Athabasca University (AU), Canada's Open University with over 40,000 students, has the mission to reduce barriers to university-level education. As an open university, students are not required to have formal prerequisites to register in entry-level courses, but they are still expected to perform satisfactorily once they enter. To aid chemistry students a simple online self-diagnostic tool (Fig. 1) was developed to allow students to independently and quickly measure their potential of success.1 The online tool employs well-established performance predictors in areas such as student educational background, conceptual basics, critical thinking, mathematical skills and problem solving skills. This particular self-diagnostic shows a strong correlation coefficient between the test score and the overall final grade in the course. Particular components of the test seem to predict particular strengths in future performance. For example, the score in critical thinking correlates well with writing examinations, while the score in conceptual basics correlates well with laboratory work. It may be noted we have found the final grade in high school chemistry is a very poor predictor of future success in our post-secondary chemistry courses. The self-diagnostic test is openly available.2 While it is not a test of high school chemistry knowledge per se, it does offer a good indication of potential future success for the high school student. In addition, it also provides an indication of weaknesses and strengths. Knowing the score of the test and predicted trouble areas, individual students can exercise more control over their studies.
Still, this diagnostic, like other one-time tests is only a simple snapshot. Once students are aware of their own performance they are already changing the potential future that was originally predicted. However, more chemistry courses and course components are going online. One advantage of having learners in a digital environment is that the activities generate a lot of data that can be tracked and analyzed. The field of “learning analytics” is promising to offer teachers and institutions a way to inform their decisions around teaching and learning online.3 It may sound like Big Brother, but in the right hands learning analytics can provide direct insight into student choice, preferences and performance in a course without having to rely on proxy measures like course satisfaction surveys, which mostly measure student perceptions. A teacher in the classroom already gets a lot of this continuous feedback from students as they progress through their studies, but the challenge of meeting individual needs in a cohort environment still remains.
At the moment learning analytics are mostly being used in a rudimentary manual batch process to improve courses, identify students at risk, and determine preferred learning styles. However, some educators are looking at more automated and continuous processes with the goal of eventually developing online courses that can provide a personalized learning environment.4 We may not be that far away from having online courses literally adapt to the student, rather than the other way around.
- D. Kennepohl, M. Guay and V. Thomas, Using an online, self-diagnostic test for introductory general chemistry at an open university. Journal of Chemical Education, November 2010, pages 1273-1277.
- G. Siemens and P. Long, Penetrating the fog: Analytics in learning and education. EDUCAUSE Review, October 2011 https://net.educause.edu/ir/library/pdf/ERM1151.pdf
- M.M. El-Bishouty, K. Saito, T. Chang and S. Graf, Teaching improvement technologies for adaptive and personalized learning environments. In Kinshuk & R. Huang (Eds.) Ubiquitous Learning Environments and Technologies (pages 225-242). 2015, Berlin Heidelberg: Springer-Verlag.