Berkeley School of Education

Machine learning (ML) techniques are increasingly prevalent in education, from their use in predicting student dropout to assisting in university admissions and facilitating the rise of massive open online courses (MOOCs). Given the rapid growth of these novel uses, there is a pressing need to investigate how ML techniques support long-standing education principles and goals. In this work, we shed light on this complex landscape drawing on qualitative insights from interviews with education experts. These interviews comprise in-depth evaluations of ML for education (ML4Ed) papers published in preeminent applied ML conferences over the past decade. Our central research goal is to critically examine how the stated or implied education and societal objectives of these papers are aligned with the ML problems they tackle. That is, to what extent does the technical problem formulation, objectives, approach, and interpretation of results align with the education problem at hand? We find that a cross-disciplinary gap exists and is particularly salient in two parts of the ML life cycle: the formulation of an ML problem from education goals and the translation of predictions to interventions. We use these insights to propose an extended ML life cycle, which may also apply to the use of ML in other domains. Our work joins a growing number of meta-analytical studies across education and ML research as well as critical analyses of the societal impact of ML. Specifically, it fills a gap between the prevailing technical understanding of machine learning and the perspective of education researchers working with students and in policy.

Students come to science classrooms with ideas informed by their prior instruction and everyday observations. Following constructivist pedagogy, assessments that encourage students to elaborate their ideas, distinguish among them, and link the most promising ones can capture students' potential and help teachers plan their lessons. In this investigation, we study an assessment that engages students in a dialog to refine their response to a Knowledge Integration (KI) question. Our Research Practice Partnership (RPP) initially trained a Natural Language Processing (NLP) idea detection model on 1218 student responses from 5 schools and identified 13 student ideas. The original model had an overall micro-averaged F-score of 0.7634. After classroom testing, three RPP expert teachers with 10+ years of experience reviewed the classroom data and expanded the model, adding six additional ideas including two that they described as precursor ideas because they foreshadowed more sophisticated reasoning. We trained the idea detection model on these 19 ideas using a dataset from 13 teachers and 1206 students across 8 public schools. The updated model had a somewhat lower overall micro-averaged F-score of 0.7297. The two precursor ideas were among the top four detected ideas. The assessment, using the updated model, guided students to express significantly more ideas. A regression model showed that the updated model was associated with greater KI score gains. Expanding the model, thus, created an assessment that motivated students to express more ideas and to achieve higher KI scores. It also provides teachers with deeper insights into their students' understanding of science.

Embodiment perspectives from the cognitive sciences offer a rethinking of the role of sensorimotor activity in human learning, knowing, and reasoning. Educational researchers have been evaluating whether and how these perspectives might inform the theory and practice of STEM instruction. Some of these researchers have created technological systems, where students solve sensorimotor interaction problems as cognitive entry into curricular content. However, the field has yet to agree on a conceptually coherent and empirically validated design framework, inspired by embodiment perspectives, for developing these instructional resources. A stumbling block toward such consensus, we propose, is an implicit disagreement among educational researchers on the relation between physical movement and conceptual learning. This hypothesized disagreement could explain the contrasting choices we witness among current designs for learning with respect to instructional methodology for cultivating new physical actions – whereas some researchers use an approach of direct instruction, such as explicit teaching of gestures, others use an indirect approach, where students must discover effective movements to solve a task. Prior to comparing these approaches, it may help first to clarify key constructs. In this theoretical essay we draw on embodiment and systems literature as well as findings from our design research so as to offer the following taxonomy that may facilitate discourse about movement in STEM learning: (1) distal movement is the technologically extended effect of physical movement on the environment; (2) proximal movement is the physical movements themselves; and (3) sensorimotor schemes are the routinized patterns of cognitive activity that become enacted through proximal movement by orienting on so-called attentional anchors. Attentional anchors are goal-oriented phenomenological objects or enactive perceptions (“sensori-”) that organize proximal movement to effect distal movement (“-motor”). All three facets of movement must be considered in analyzing embodied learning processes. We demonstrate that indirect movement instruction enables students to develop new sensorimotor schemes including attentional anchors as idiosyncratic solutions to physical interaction problems. These schemes are, by necessity, grounded in students’ own agentive relation to the world while also grounding target content such as mathematical notions.