From Gatekeeping to Engagement: A Multicontextual, Mixed Method Study of Student Academic Engagement in Introductory STEM Courses.

The lack of academic engagement in introductory science courses is considered by some to be a primary reason why students switch out of science majors. This study employed a sequential, explanatory mixed methods approach to provide a richer understanding of the relationship between student engagement and introductory science instruction. Quantitative survey data were drawn from 2,873 students within 73 introductory science, technology, engineering, and mathematics (STEM) courses across 15 colleges and universities, and qualitative data were collected from 41 student focus groups at eight of these institutions. The findings indicate that students tended to be more engaged in courses where the instructor consistently signaled an openness to student questions and recognizes her/his role in helping students succeed. Likewise, students who reported feeling comfortable asking questions in class, seeking out tutoring, attending supplemental instruction sessions, and collaborating with other students in the course were also more likely to be engaged. Instructional implications for improving students' levels of academic engagement are discussed

The Recommendation Architecture: Lessons from Large-Scale Electronic Systems Applied to Cognition

A fundamental approach of cognitive science is to understand cognitive systems by separating them into modules. Theoretical reasons are described which force any system which learns to perform a complex combination of real time functions into a modular architecture. Constraints on the way modules divide up functionality are also described. The architecture of such systems, including biological systems, is constrained into a form called the recommendation architecture, with a primary separation between clustering and competition. Clustering is a modular hierarchy which manages the interactions between functions on the basis of detection of functionally ambiguous repetition. Change to previously detected repetitions is limited in order to maintain a meaningful, although partially ambiguous context for all modules which make use of the previously defined repetitions. Competition interprets the repetition conditions detected by clustering as a range of alternative behavioural recommendations, and uses consequence feedback to learn to select the most appropriate recommendation. The requirements imposed by functional complexity result in very specific structures and processes which resemble those of brains. The design of an implemented electronic version of the recommendation architecture is described, and it is demonstrated that the system can heuristically define its own functionality, and learn without disrupting earlier learning. The recommendation architecture is compared with a range of alternative cognitive architectural proposals, and the conclusion reached that it has substantial potential both for understanding brains and for designing systems to perform cognitive functions

Conveying troublesome concepts : using an open-space learning activity to teach mixed-methods research in the health sciences

In the past decade, there has been a groundswell of interest in the use of mixed-methods approaches to conduct research in the health sciences. However, there remains a paucity of diverse teaching materials, curricula and activities to support the continued expansion of education and innovation in mixed-methods research. Here, we report the development and evaluation of an open-space learning activity and tool to aid teaching the concept of synthesis in mixed-methods research. We detail the iterations of the teaching activity and tool as they were developed, we report student feedback, and we discuss the utility of the activity and tool for introducing the concept of synthesis in mixed-methods research within health science and related fields

Learning of classification models from group-based feedback

Learning of classification models in practice often relies on a nontrivial amount of human annotation effort. The most widely adopted human labeling process assigns class labels to individual data instances. However, such a process is very rigid and may end up being very time-consuming and costly to conduct in practice. Finding more effective ways to reduce human annotation effort has become critical for building machine learning systems that require human feedback. In this thesis, we propose and investigate a new machine learning approach - Group-Based Active Learning - to learn classification models from limited human feedback. A group is defined by a set of instances represented by conjunctive patterns that are value ranges over the input features. Such conjunctive patterns define hypercubic regions of the input data space. A human annotator assesses the group solely based on its region-based description by providing an estimate of the class proportion for the subpopulation covered by the region. The advantage of this labeling process is that it allows a human to label many instances at the same time, which can, in turn, improve the labeling efficiency. In general, there are infinitely many regions one can define over a real-valued input space. To identify and label groups/regions important for classification learning, we propose and develop a Hierarchical Active Learning framework that actively builds and labels a hierarchy of input regions. Briefly, our framework starts by identifying general regions covering substantial portions of the input data space. After that, it progressively splits the regions into smaller and smaller sub-regions and also acquires class proportion labels for the new regions. The proportion labels for these regions are used to gradually improve and refine a classification model induced by the regions. We develop three versions of the idea. The first two versions aim to build a single hierarchy of regions. One builds it statically using hierarchical clustering, while the other one builds it dynamically, similarly to the decision tree learning process. The third approach builds multiple hierarchies simultaneously, and it offers additional flexibility for identifying more informative and simpler regions. We have conducted comprehensive empirical studies to evaluate our framework. The results show that the methods based on the region-based active learning can learn very good classifiers from a very few and simple region queries, and hence are promising for reducing human annotation effort needed for building a variety of classification models