Janice D. Gobert

Experimental Comparison of Inquiry and Direct Instruction in Science.

There are continuing educational and political debates about ‘inquiry’ versus ‘direct’ teaching of science. Traditional science instruction has been largely direct but in the US, recent national and state science education standards advocate inquiry throughout K‐12 education. While inquiry‐based instruction has the advantage of modelling aspects of the nature of real scientific inquiry, there is little unconfounded comparative research into the effectiveness and efficiency of the two instructional modes for developing science conceptual understanding. This research undertook a controlled experimental study comparing the efficacy of carefully designed inquiry instruction and equally carefully designed direct instruction in realistic science classroom situations at the middle school grades. The research design addressed common threats to validity. We report on the nature of the instructional units in each mode, research design, methods, classroom implementations, monitoring, assessments, analysis and project findings.

Leveraging machine-learned detectors of systematic inquiry behavior to estimate and predict transfer of inquiry skill

We present work toward automatically assessing and estimating science inquiry skills as middle school students engage in inquiry within a physical science microworld. Towards accomplishing this goal, we generated machine-learned models that can detect when students test their articulated hypotheses, design controlled experiments, and engage in planning behaviors using two inquiry support tools. Models were trained using labels generated through a new method of manually hand-coding log files, “text replay tagging”. This approach led to detectors that can automatically and accurately identify these inquiry skills under student-level cross-validation. The resulting detectors can be applied at run-time to drive scaffolding intervention. They can also be leveraged to automatically score all practice attempts, rather than hand-classifying them, and build models of latent skill proficiency. As part of this work, we also compared two approaches for doing so, Bayesian Knowledge-Tracing and an averaging approach that assumes static inquiry skill level. These approaches were compared on their efficacy at predicting skill before a student engages in an inquiry activity, predicting performance on a paper-style multiple choice test of inquiry, and predicting performance on a transfer task requiring data collection skills. Overall, we found that both approaches were effective at estimating student skills within the environment. Additionally, the models’ skill estimates were significant predictors of the two types of inquiry transfer tests.

Examining the Relationship Between Students' Understanding of the Nature of Models and Conceptual Learning in Biology, Physics, and Chemistry

This research addresses high school students’ understandings of the nature of models, and their interaction with model‐based software in three science domains, namely, biology, physics, and chemistry. Data from 736 high school students’ understandings of models were collected using the Students’ Understanding of Models in Science (SUMS) survey as part of a large‐scale, longitudinal study in the context of technology‐based curricular units in each of the three science domains. The results of ANOVA and regression analyses showed that there were differences in students’ pre‐test understandings of models across the three domains, and that higher post‐test scores were associated with having engaged in a greater number of curricular activities, but only in the chemistry domain. The analyses also showed that the relationships between the pre‐test understanding of models subscales scores and post‐test content knowledge varied across domains. Some implications are discussed with regard to how students’ understanding of the nature of models can be promoted.

Introduction to the Issue

For this special issue of JSET, we have assembled five important papers based on research at the Concord Consortium. These papers report new developments in strands of ongoing research and development that are among the most promising ways to realize the educational potential of information and communication technologies (ICT). The goal of this Introduction is to place these papers in their larger context and to indicate some of our expectations for future developments in these areas.

Leveraging Technology and Cognitive Tehory on Visualization to Promote Students’ Science

This chapter defines visualization as it is used in psychology and education. It delineates the role of visualization research in science education as being primarily concerned with external representations and how to best support students’ while learning with visualizations. In doing so, relevant literature from Cognitive Science is reviewed. Two science education projects, namely, Making Thinking Visible and Modeling Across the Curriculum are then described as exemplars of projects that leverage cognitive theory and technology to support students’ science learning and scientific literacy.

Discovery With Models A Case Study on Carelessness in Computer-Based Science Inquiry

In recent years, an increasing number of analyses in learning analytics and educational data mining (EDM) have adopted a “discovery with models” approach, where an existing model is used as a key component in a new EDM or analytics analysis. This article presents a theoretical discussion on the emergence of discovery with models, its potential to enhance research on learning and learners, and key lessons learned in how discovery with models can be conducted validly and effectively. We illustrate these issues through discussion of a case study where discovery with models was used to investigate a form of disengaged behavior (i.e., carelessness) in the context of middle school computer-based science inquiry. This behavior was acknowledged as a problem in education as early as the 1920s. With the increasing use of high-stakes testing, the cost of student carelessness can be higher. For instance, within computer-based learning environments, careless errors can result in reduced educational effectiveness, with students continuing to receive material they have already mastered. Despite the importance of this problem, it has received minimal research attention, in part because of difficulties in operationalizing carelessness as a construct. Building from theory on carelessness and a Bayesian framework for knowledge modeling, we use machine-learned detectors to predict carelessness within authentic use of a computer-based learning environment. We then use a discovery with models approach to link these validated carelessness measures to survey data to study the correlations between the prevalence of carelessness and student goal orientation.

Learning Genetics from Dragons: From Computer-Based Manipulatives to Hypermodels

This chapter addresses an issue central to the design of educational technology: the extent to which one should explicitly guide the student as opposed to simply creating an open-ended tool for discovery and experimentation. The basis for the discussion is the experience of the lead author, described in an earlier publication, regarding the use of a program called GenScope. GenScope offered students a multilevel model of genetics, ranging from DNA to populations, wherein manipulations made at any one level could affect other levels, much as a change in one cell of a spreadsheet may cause a “ripple effect” on other cells down the line. In common with other general-purpose computer models, GenScope embodied no specific educational agenda. The chapter describes a more recent program, BioLogica, which augments the functionality of GenScope by monitoring and logging students’ actions, providing online, context-sensitive scaffolding, and situating student activities within a context of real-world examples. We present results from a 5-year program of research conducted with BioLogica in high school biology classes throughout the United States.

Sensor-Free Affect Detection for a Simulation-Based Science Inquiry Learning Environment

Recently, there has been considerable interest in understanding the relationship between student affect and cognition. This research is facilitated by the advent of automated sensor-free detectors that have been designed to “infer” affect from the logs of student interactions within a learning environment. Such detectors allow for fine-grained analysis of the impact of different affective states on a range of learning outcome measures. However, these detectors have to date only been developed for a subset of online learning environments, including problem-solving tutors, dialogue tutors, and narrative-based virtual environments. In this paper, we extend sensor-free affect detection to a science microworld environment, affording the possibility of more deeply studying and responding to student affect in this type of learning environment.

Carelessness and goal orientation in a science microworld

In this paper, we study the relationship between goal orientation within a science inquiry learning environment for middle school students and carelessness, i.e., not demonstrating an inquiry skill despite knowing it. Carelessness is measured based on a machine-learned model. We find, surprisingly, that carelessness is higher for students with strong mastery or learning goals, compared to students who lack strong goal orientation.

Towards Scalable Assessment of Performance-Based Skills: Generalizing a Detector of Systematic Science Inquiry to a Simulation with a Complex Structure

There are well-acknowledged challenges to scaling computerized performance-based assessments. One such challenge is reliably and validly identifying ill-defined skills. We describe an approach that leverages a data mining framework to build and validate a detector that evaluates an ill-defined inquiry process skill, designing controlled experiments. The detector was originally built and validated for use with physical science simulations that have a simpler, linear causal structure. In this paper, we show that the detector can be used to identify demonstration of skill within a life science simulation on Ecosystems that has a complex underlying causal structure. The detector is evaluated in three ways: 1) identifying skill demonstration for a new student cohort, 2) handling the variability in how students conduct experiments, and 3) using it to determine when students are off-track before they finish collecting data.