Discovery Learning

A key learning outcome in problem-solving domains is the development of flexible knowledge, where learners know multiple strategies and adaptively choose efficient strategies. Two interventions hypothesized to improve flexibility in problem solving were experimentally evaluated: prompts to discover multiple strategies and direct instruction on multiple strategies. Participants were 132 sixth-grade students who solved linear equations for three hours. Both interventions improved students' flexibility in problem solving and did not replace, nor interfere with, one another. Overall, the study provides causal evidence that exposure to multiple strategies leads to improved flexibility in problem solving and that discovery learning and direct instruction are compatible instructional approaches.

Weeds are an emerging constraint on crop production, as a result of population pressure and more intensive use of cultivated land. A diagnostic study was carried out from June through August 2002 in the five agro-ecological zones of Benin (1) to identify the relative importance of weeds among major production constraints, (2) to better understand farmers' perceptions of weed problems, and (3) to take cognizance of their reactions and the different actors involved in weed management technology development. The study also aimed at suggesting the development of weed management strategies that work and are acceptable under small-scale farmers' conditions. Data were collected through semi-structured and unstructured group and/or individual interviews, and through participant observation, transect studies and weed identification during field visits. The results show considerable diversity in biophysical constraints and socio-economic conditions. Population density has led to high pressure on arable land, resulting in land degradation and weed problems. In all situations, pernicious (Imperata cylindrica, Cyperus spp., Commelina spp.) and parasitic (Striga spp.) weeds are difficult to eradicate, causing substantial food crop yield losses and threatening the livelihood of people. Land and labour shortage, low commodity prices and lack of credit were the main constraints hindering weed management. Causes, effects and consequences were analysed, taking into account the socio-economic context. The study's findings with respect to weed management measures, and their adaptation and constraints in using them, suggest that effective and acceptable weed management strategies should be developed, taking into account both biological and social science perspectives with a focus on adding value to indigenous knowledge.

Forty 4th and 9th grade students participated individually in tutorial interviews centered on a problem-solving activity designed for learning basic algebra mechanics through diagrammatic modeling of an engaging narrative about a buccaneering giant burying and unearthing her treasure on a desert island. Participants were randomly assigned to experimental (Discovery) and control (No-Discovery) conditions. Mixed-method analyses revealed greater learning gains for Discovery participants. Elaborating on a heuristic activity architecture for technology-based guided-discovery learning (Chase and Abrahamson 2015), we reveal a network of interrelated inferential constraints that learners iteratively calibrate as they each refine and reflect on their evolving models. We track the emergence of these constraints by analyzing annotated transcriptions of two case-study student sessions and argue for their constituting role in conceptual development.

In recent years, tangible user interfaces (TUI) have gained in popularity in educational contexts, among others to implement problem-solving and discovery learning science activities. In the context of an interdisciplinary and cross-institutional collaboration, we conducted a multimodal EMCA-based video user study involving a TUI-mediated bicycle mechanics simulation. This article focusses on the discovering work of a group of three students with regard to a particular tangible object (a red button), designed to support participants engagement with the underlying physics aspects and its consequences with regard to their engagement with the targeted mechanics aspects.

Scaffolding is the asymmetrical social co-enactment of natural or cultural practice, wherein a more able agent implements or performs for a novice elements of a challenging activity. What the novice may not learn, however, is how the expert’s co-enactments support the activity. Granted, in many cultural practices novices need not understand underlying process. But where process is content, such as mathematics, scaffolding is liable to undermine tenets of reform-oriented pedagogy. We point to tensions between traditional conceptualizations of scaffolding and discovery-based pedagogical methodology for mathematics education. Focusing on co-enactment as a critical feature of scaffolding activities, we introduce “reverse scaffolding”, wherein experts enact for novices only what they know to do rather than what they do not know to do. We demonstrate our approach by discussing a novel technological learning activity, Giant Steps for Algebra, wherein students construct models of realistic narratives. We argue for the method’s potential via reporting on findings from mixed-methods analyses of a quasi-experimental implementation with 40 students.

In computational modeling activities, learners are expected to discover the inner workings of scientific and mathematical systems: First elaborating their understandings of a given system through constructing a computer model, then ''debugging'' that knowledge by testing and refining the model. While such activities have been shown to support science learning, difficulties building and using computational models are common and reduce learning benefits. Drawing from Collins and Ferguson (Educ Psychol 28(1):25–42, 1993), we conjecture that a major cause for such difficulties is a misalignment between the epistemic games (modeling strategies) learners play, and the epistemic forms (model types) a given modeling environment is designed to support. To investigate, we analyzed data from a study in which ten groups of U. S. fifth graders (n = 28) worked to create stop motion animations and agent-based computational models (ABMs) to discover the particulate nature of matter. Content analyses revealed that (1) groups that made progress— that is, that developed increasingly mechanistic, explanatory models—focused on elements , movement, and interactions when developing their models, a strategy well-aligned with both animation and ABM; (2) groups that did not make progress focused on sequences of phases, a strategy well-aligned with animation but not with ABM; and (3) struggling groups progressed when they received guidance about modeling strategies, but not when they received guidance about model content. We present summary analyses and three vignettes to illustrate these findings, and share implications for research and curricular design.

This paper presents a pedagogical agent designed to support students in an embodied, discovery-based learning environment. Discovery-based learning guides students through a set of activities designed to foster particular insights. In this case, the animated agent explains how to use the Mathematical Imagery Trainer for Proportionality, provides performance feedback, leads students to have different experiences and provides remedial instruction when required. It is a challenging task for agent technology as the amount of concrete feedback from the learner is very limited, here restricted to the location of two markers on the screen. A Dynamic Decision Network is used to automatically determine agent behavior, based on a deep understanding of the tutorial protocol. A pilot evaluation showed that all participants developed movement schemes supporting proto-proportional reasoning. They were able to provide verbal proto-proportional expressions for one of the taught strategies, but not the other.

In the context of a design-based research effort to develop a technology-enabled constructivist algebra unit, a new activity architecture emerged that steps students through discovery levels. As they build a virtual model of a problem situation, students figure out technical principles for assuring the model’s fidelity to the situation. These construction heuristics, we find, are precisely the conceptual foundations of algebra, such as tinkering with the model to assure that the variable quantity is of consistent size throughout the model. We articulated these principles as situated intermediary learning objectives (SILOs). At each interaction level, the student discovers a SILO, and then the technology takes over by automatizing that SILO, thus freeing the student for further discovery. We call this architecture reverse scaffolding, because the cultural mediator thus relieves learners from performing what they know to do, not from what they do not know to do. In a quasi-experimental evaluation study (Grades 4 & 9; n=40), reverse-scaffolding students outperformed direct-scaffolding students, for whom the technical features were pre-automatized. We speculate on the architecture’s generalizability.

In this paper we report on the operation and results of a pilot trial on the use of Science-Fiction Prototyping (SFP) as a means to motivate students to engage with STEM and language learning courses. In particular we describe two case studies. The first was conducted in Shijiazhuang University, China, and involved approximately 102 students following a course aimed at improving their English language abilities for computer science applications. The second case study concerned the use of micro science-fiction prototypes, in the form of Twitter-fiction, as a means of motivating pre-university students to take up STEM studies and careers. Finally, the paper concludes by describing future directions of this work

Investigation of direct causes is a fundamental component of inquiry and analysis tasks that require skilled observations, logical thinking, and a persistent search for a complete understanding of the events. The need to cultivate such skills and persistence is a major challenge for diverse disciplines from accident investigation to forensics to intelligence analysis. In this context, persistence means to keep pulling the threads of evidence until a sufficient understanding of cause-effect relationships has emerged. The training challenge is rooted in fundamental questions about performance measurement and instruction: Can we effectively instill the required skills and persistence by merely informing learners through traditional classroom instruction? Or would such cognitive skills and persistence be better developed and refined through carefully crafted experience-based training? In instructional systems design terminology, this question may be phrased as a choice between receptive/directive instructional architectures that focus on ASK and TELL approaches versus approaches that emphasize SHOW and DO. The latter, more interactive instructional approaches emphasize active learning and performance assessment. We suggest that active, performance-based paradigms such as scenario-based and guided-discovery learning approaches may provide more effective solutions. By immersing the learner in appropriate interactive scenarios, we can ascertain through actual performance the extent to which the learner demonstrates the objective knowledge or skills. We have previously reported on an application of guideddiscovery principles to develop web-based awareness training for security inquiry officials. The purpose of this paper is to report on subsequent research that employs guided-discovery scenarios to enhance the learner's evidential reasoning process through practice in following threads to identify direct causes. Implications for inquiry/analysis and cognitive skills training are discussed.