Measurement of Ability in Adaptive Learning and Assessment Systems when Learners Use On-Demand Hints.

Adaptive learning and assessment systems support learners in acquiring knowledge and skills in a particular domain. The learners' progress is monitored through them solving items matching their level and aiming at specific learning goals. Scaffolding and providing learners with hints are powerful tools in helping the learning process. One way of introducing hints is to make hint use the choice of the student. When the learner is certain of their response, they answer without hints, but if the learner is not certain or does not know how to approach the item they can request a hint. We develop measurement models for applications where such on-demand hints are available. Such models take into account that hint use may be informative of ability, but at the same time may be influenced by other individual characteristics. Two modeling strategies are considered: (1) The measurement model is based on a scoring rule for ability which includes both response accuracy and hint use. (2) The choice to use hints and response accuracy conditional on this choice are modeled jointly using Item Response Tree models. The properties of different models and their implications are discussed. An application to data from Duolingo, an adaptive language learning system, is presented. Here, the best model is the scoring-rule-based model with full credit for correct responses without hints, partial credit for correct responses with hints, and no credit for all incorrect responses. The second dimension in the model accounts for the individual differences in the tendency to use hints.

Self-Directed Learning Favors Local, Rather Than Global, Uncertainty.

Collecting (or "sampling") information that one expects to be useful is a powerful way to facilitate learning. However, relatively little is known about how people decide which information is worth sampling over the course of learning. We describe several alternative models of how people might decide to collect a piece of information inspired by "active learning" research in machine learning. We additionally provide a theoretical analysis demonstrating the situations under which these models are empirically distinguishable, and we report a novel empirical study that exploits these insights. Our model-based analysis of participants' information gathering decisions reveals that people prefer to select items which resolve uncertainty between two possibilities at a time rather than items that have high uncertainty across all relevant possibilities simultaneously. Rather than adhering to strictly normative or confirmatory conceptions of information search, people appear to prefer a "local" sampling strategy, which may reflect cognitive constraints on the process of information gathering.

SIRT3 substrate specificity determined by peptide arrays and machine learning.

Accumulating evidence suggests that reversible protein acetylation may be a major regulatory mechanism that rivals phosphorylation. With the recent cataloging of thousands of acetylation sites on hundreds of proteins comes the challenge of identifying the acetyltransferases and deacetylases that regulate acetylation levels. Sirtuins are a conserved family of NAD(+)-dependent protein deacetylases that are implicated in genome maintenance, metabolism, cell survival, and lifespan. SIRT3 is the dominant protein deacetylase in mitochondria, and emerging evidence suggests that SIRT3 may control major pathways by deacetylation of central metabolic enzymes. Here, to identify potential SIRT3 substrates, we have developed an unbiased screening strategy that involves a novel acetyl-lysine analogue (thiotrifluoroacetyl-lysine), SPOT-peptide libraries, machine learning, and kinetic validation. SPOT peptide libraries based on known and potential mitochondrial acetyl-lysine sites were screened for SIRT3 binding and then analyzed using machine learning to establish binding trends. These trends were then applied to the mitochondrial proteome as a whole to predict binding affinity of all lysine sites within human mitochondria. Machine learning prediction of SIRT3 binding correlated with steady-state kinetic k(cat)/K(m) values for 24 acetyl-lysine peptides that possessed a broad range of predicted binding. Thus, SPOT peptide-binding screens and machine learning prediction provides an accurate and efficient method to evaluate sirtuin substrate specificity from a relatively small learning set. These analyses suggest potential SIRT3 substrates involved in several metabolic pathways such as the urea cycle, ATP synthesis, and fatty acid oxidation.

ABNER: an open source tool for automatically tagging genes, proteins and other entity names in text.

ABNER (A Biomedical Named Entity Recognizer) is an open source software tool for molecular biology text mining. At its core is a machine learning system using conditional random fields with a variety of orthographic and contextual features. The latest version is 1.5, which has an intuitive graphical interface and includes two modules for tagging entities (e.g. protein and cell line) trained on standard corpora, for which performance is roughly state of the art. It also includes a Java application programming interface allowing users to incorporate ABNER into their own systems and train models on new corpora.