The ACT Programming Tutor provides assistance to students as they write short computer programs. The tutor is constructed around a set of several hundred programming rules that allows the tutor to solve exercises step-by-step along with the student. This p^)er evaluates the Oitor's student modeling jx^ocedure that is used to guide remediation. This procedure, termed knowledge tracing, employs an overlay of the tutor's programming rules. In knowledge tracing, the tutor maintains an estimate of the probability that the student has learned each of the rules. The probability for a rule is updated at each opportunity to apply the rule, based on the student's performance. The predictive validity of the modeling procedure for tutw performance accuracy and posttest performance accuracy is assessed. Individual differences in learning parameters and cognitive rules are discussed, along with possible improvements in the modeling procedure.

Control and content of feedback was manipulated as students practiced coding functions with the Lisp Four feedback conditions were employed: (1) immediate error feedback and correction, (2) immediate error flagging but immediate correction not required (3) feedback on demand aixl (4) no tutorial assistance. The wide range in feedback conditions did not affect mean learning rate as measured by individual production firing, time to complete the exercises or post-test performance. However, post-test results were more highly correlated with student ability as tutorial assistance decreased across conditions. Feedback conditions also affected students' monitoring of the learning process. Across groups, students found the material was easier and bcUeved they had learned it better as assistance decreased across conditions. However, students w h o received more assistance estimated their mastery of the material more accurately. Finally, students reported relatively little preference for one tutoring condition over the others. Students w h o could exercise the most control over feedback reacted fairly passively to the tutoring conditions; students in condition 3 tended not to ask for much help and students in condition 2 tended to correct error immediately although it was not required.

In high-stakes applications of machine learning models, interpretability methods provide guarantees that models are right for the right reasons. In medical imaging, saliency maps have become the standard tool for determining whether a neural model has learned relevant robust features, rather than artefactual noise. However, saliency maps are limited to local model explanation because they interpret predictions on an image-by-image basis. We propose aggregating saliency globally, using semantic segmentation masks, to provide quantitative measures of model bias across a dataset. To evaluate global saliency methods, we propose two metrics for quantifying the validity of saliency explanations. We apply the global saliency method to skin lesion diagnosis to determine the effect of artefacts, such as ink, on model bias.

Syphilis has many atypical morphologies which can present a diagnostic challenge, especially in patients with HIV/AIDS who may have multiple concurrent conditions. We describe a 41-year-old man with recently diagnosed HIV who was admitted for acute right vision loss and a diffuse rash with involvement of the palms and soles. He received diagnoses of secondary syphilis and Kaposi sarcoma in the setting of AIDS. Examination revealed an unusual dark brown-to-purple umbilicated papule with a necrotic center on the abdomen, raising a diagnostic dilemma. Skin biopsy showed secondary syphilis, despite the concurrent diagnosis of Kaposi sarcoma. The patient was treated with antibiotic and antiretroviral therapy and symptoms resolved. This case aims to share the clinical reasoning behind diagnosing a patient with HIV/AIDS with multiple concurrent conditions and to raise awareness of the many atypical cutaneous manifestations of secondary syphilis.

This paper evaluates the student modeling procedure in the ACT Programming Tutor (APT). APT is a practice environment that provides assistance to students as they write short programs. The tutor is constructed around a set of several hundred programming rules called the ideal student model, that allows the program to solve exercises along with the student. As the student works the tutor maintains an estimate of the probability that the student has learned the rules in the ideal model, in a process we call knowledge tracing. The cognitive model, and the learning and performance assumptions that underlie knowledge tracing are described. The assumptions that underlie knowledge tracing also yield performance predictions. These predictions provide a good fit to students' performance in completing tutor exercises, but a more important issue is how well the model predicts students' performance outside the tutor enviroimient. A previous study showed that the model provides a good fit to average posttest performance across students, but is less sensitive to individual differences. This paper describes a method of individualizing learning and performance estimates on-line in the tutor and assesses the validity of the resulting performance predictions.

Artificial intelligence is becoming increasingly important in dermatology, with studies reporting accuracy matching or exceeding dermatologists for the diagnosis of skin lesions from clinical and dermoscopic images. However, real-world clinical validation is currently lacking. We review dermatological applications of deep learning, the leading artificial intelligence technology for image analysis, and discuss its current capabilities, potential failure modes, and challenges surrounding performance assessment and interpretability. We address the following three primary applications: (i) teledermatology, including triage for referral to dermatologists; (ii) augmenting clinical assessment during face-to-face visits; and (iii) dermatopathology. We discuss equity and ethical issues related to future clinical adoption and recommend specific standardization of metrics for reporting model performance.