Coding energy knowledge in constructed responses with explainable NLP models

Background: Formative assessments are needed to enable monitoring how studentknowledge develops throughout a unit. Constructed response items which requirelearners to formulate their own free‐text responses are well suited for testing their activeknowledge. However, assessing such constructed responses in an automated fashion is acomplex task and requires the application of natural language processing methodology.In this article, we implement and evaluate multiple machine learning models for codingenergy knowledge in free‐text responses of German K‐12 students to items in formativescience assessments which were conducted during synchronous online learning sessions.Dataset: The dataset we collected for this purpose consists of German constructedresponses from 38 different items dealing with aspects of energy such as manifestation and transformation. The units and items were implemented with the help of project‐based pedagogy and evidence‐centered design, and the responses were codedfor seven core ideas concerning the manifestation and transformation of energy. Thedata was collected from students in seventh, eighth and ninth grade.Methodology: We train various transformer‐ and feature‐based models and comparetheir ability to recognize the respective ideas in students' writing. Moreover, asdomain knowledge and its development can be formally modeled through knowledgenetworks, we evaluate how well the detection of the ideas within responses translated into accurate co‐occurrence‐based knowledge networks. Finally, in terms of thedescriptive accuracy of our models, we inspect what features played a role for whichprediction outcome and if the models pick up on undesired shortcuts. In addition tothis, we analyze how much the models match human coders in what evidence withinresponses they consider important for their coding decisions.Results: A model based on a modified GBERT‐large can achieve the overall mostpromising results, although descriptive accuracy varies much more than predictiveaccuracy for the different ideas assessed. For reasons of comparability, we also evaluate the same machine learning architecture using the SciEntsBank 3‐Wa

Head-to-Head Comparison of BRAF/MEK Inhibitor Combinations Proposes Superiority of Encorafenib Plus Trametinib in Melanoma

BRAFV600 mutations in melanoma are targeted with mutation-specific BRAF inhibitors in combination with MEK inhibitors, which have significantly increased overall survival, but eventually lead to resistance in most cases. Additionally, targeted therapy for patients with NRASmutant melanoma is difficult. Our own studies showed that BRAF inhibitors amplify the effects of MEK inhibitors in NRASmutant melanoma. This study aimed at identifying a BRAF and MEK inhibitor combination with superior anti-tumor activity to the three currently approved combinations. We, thus, assessed anti-proliferative and pro-apoptotic activities of all nine as well as resistance-delaying capabilities of the three approved inhibitor combinations in a head-to-head comparison in vitro. The unconventional combination encorafenib/trametinib displayed the highest activity to suppress proliferation and induce apoptosis, acting in an additive manner in BRAFmutant and in a synergistic manner in NRASmutant melanoma cells. Correlating with current clinical studies of approved inhibitor combinations, encorafenib/binimetinib prolonged the time to resistance most efficiently in BRAFmutant cells. Conversely, NRASmutant cells needed the longest time to establish resistance when treated with dabrafenib/trametinib. Together, our data indicate that the most effective combination might not be currently used in clinical settings and could lead to improved overall responses