A study into the use of computer aided assessment to enhance formative assessment during the early stages of undergraduate chemistry courses

A Virtual Learning Environment (WebCT and latter Moodle) was used to provide students with instant, meaningful feedback on their study of chemistry units during their first semester at University. Short multiple choice questions (MCQ’s) were written covering each segment of material delivered in lectures and made available to students over the University computer intranet to allow “24/7” access. The most important aspect of the work was the feedback offered to students within the questions, which was written by undergraduate students to ensure its usefulness. The vast majority of the cohort used the MCQ’s, most to gain formative feedback and some as a revision aid prior to summative examinations. During the evaluation, students reported that they found the ready access useful and helpful in learning the material. Some students used the MCQ’s in preference to visiting tutors face to face (f2f) but most expressed a preference for the usual tutorial programme over such CAL methods. Most of the cohort used the feedback from the MCQ’s to guide their revision, but again were not prepared to use CAL to replace f2f contact with tutors. Our work meets a number of the published conditions for effective feedback to occur. For example, it is immediate, timely and allows students to receive frequent feedback at a level which means that it can be used to inform further study. In the first year of using the MCQ’s, there was a significant increase in the average marks in the end of unit examinations and a decrease in the drop-out rate during Semester 1. Although firm conclusions cannot be drawn from one year’s data, these results together with the very positive reaction from the students encourage us to further develop the approach into the open source VLE Moodle, which allowed us to address some of the issues

A prediction model for early death in non-small-cell lung cancer patients following curative-intent chemoradiotherapy. (PMID 29034756)

<p><b>Background:</b> Early death after a treatment can be seen as a therapeutic failure. Accurate prediction of patients at risk for early mortality is crucial to avoid unnecessary harm and reducing costs. The goal of our work is two-fold: first, to evaluate the performance of a previously published model for early death in our cohorts. Second, to develop a prognostic model for early death prediction following radiotherapy.</p> <p><b>Material and methods:</b> Patients with NSCLC treated with chemoradiotherapy or radiotherapy alone were included in this study. Four different cohorts from different countries were available for this work (<i>N</i> = 1540). The previous model used age, gender, performance status, tumor stage, income deprivation, no previous treatment given (yes/no) and body mass index to make predictions. A random forest model was developed by learning on the Maastro cohort (<i>N</i> = 698). The new model used performance status, age, gender, T and N stage, total tumor volume (cc), total tumor dose (Gy) and chemotherapy timing (none, sequential, concurrent) to make predictions. Death within 4 months of receiving the first radiotherapy fraction was used as the outcome.</p> <p><b>Results:</b> Early death rates ranged from 6 to 11% within the four cohorts. The previous model performed with AUC values ranging from 0.54 to 0.64 on the validation cohorts. Our newly developed model had improved AUC values ranging from 0.62 to 0.71 on the validation cohorts.</p> <p><b>Conclusions:</b> Using advanced machine learning methods and informative variables, prognostic models for early mortality can be developed. Development of accurate prognostic tools for early mortality is important to inform patients about treatment options and optimize care.</p