Designs for learning about climate change as a complex system

This paper reports on a study in which students used agent-based computer models to learn about complex systems ideas of relevance to understanding climate change. The experimental condition used a Productive Failure (PF) learning design in which ninth grade students initially worked with agent-based computer models to solve challenge problems followed by teacher instruction about targeted climate and complexity ideas. In contrast, the comparison condition employed a Direct Instruction (DI) learning design in which the teacher instruction was provided initially, followed by the students working on the same computer models and challenge problems as the experimental group. The students in the PF group scored significantly higher on the post-test on measures of climate and complex systems explanatory knowledge and near and far knowledge transfer. Theoretical and practical implications of these findings are considered.     

Comparing Learning From Productive Failure and Vicarious Failure

A total of 136 eighth-grade math students from 2 Singapore schools learned from either productive failure (PF) or vicarious failure (VF). PF students generated solutions to a complex problem targeting the concept of variance that they had not learned yet before receiving instruction on the targeted concept. VF students evaluated the solutions generated by PF students before receiving the same instruction. Student-generated solutions were either suboptimal or incorrect, and in this sense can be conceived as failed problem-solving attempts. Although there was no difference on self-reported engagement, PF students reported significantly greater mental effort and interest in knowing the canonical solution to the problem than VF students. When preexisting differences in general ability, math ability, and prior knowledge were controlled, PF students outperformed VF students on conceptual understanding and transfer without compromising procedural fluency. These results suggest that when learning a new math concept, people learn better from their own failed solutions than those of others provided appropriate instruction on the targeted concept is given after the generation or evaluation activity.     

The ontologies of complexity and learning about complex systems

This paper discusses a study of students learning core conceptual perspectives from recent scientific research on complexity using a hypermedia learning environment in which different types of scaffolding were provided. Three comparison groups used a hypermedia system with agent-based models and scaffolds for problem-based learning activities that varied in terms of the types of text based scaffolds that were provided related to a set of complex systems concepts. Although significant declarative knowledge gains were found for the main experimental treatment in which the students received the most scaffolding, there were no significant differences amongst the three groups in terms of the more cognitively demanding performance on problem solving tasks. However, it was found across all groups that the students who enriched their ontologies about how complex systems function performed at a significantly higher level on transfer problem solving tasks in the posttest. It is proposed that the combination of interactive representational scaffolds associated with NetLogo agent-based models in complex systems cases and problem solving scaffolding allowed participants to abstract ontological dimensions about how systems of this type function that, in turn, was associated with the higher performance on the problem solving transfer tasks. Theoretical and design implications for learning about complex systems are discussed.     

Supporting learning of variable control in a computer‐based biology environment: Effects of prompting college students to reflect on their own thinking

While instruction on control of variables has been shown to be effective, especially when it encourages students to focus explicitly on rules or procedures, little evidence of application to novel problems has been obtained. We hypothesized that prompting students to understand their own learning processes while doing experiments involving control of variables would allow them to activate their repertoire of knowledge and strategies and learn in a way that would enhance transfer of learning. Students were assigned to one of four versions of a computer‐based biology simulation learning environment, each employing a different type of prompt: reason justification, rule based, emotion focused, or none (control). Learning in this computer environment, college biology students designed and conducted experiments involving control of variables. Students' ability to solve both contextually similar (near transfer) and contextually dissimilar (far transfer) problems was assessed. The treatment groups performed equally well on contextually similar problems. However, on a contextually dissimilar problem, the reason justification group had significantly higher scores than the other groups. Qualitative data showed that the reason justification prompts directed students' attention to understanding when, why, and how to employ experiment design principles and strategies, and this in turn helped students to transfer their understanding to a novel problem. © 1999 John Wiley & Sons, Inc. J Res Sci Teach 36: 837–858, 1999     

Model-Based Knowing: How Do Students Ground Their Understanding About Climate Systems in Agent-Based Computer Models?

This paper gives a grounded cognition account of model-based learning of complex scientific knowledge related to socio-scientific issues, such as climate change. It draws on the results from a study of high school students learning about the carbon cycle through computational agent-based models and investigates two questions: First, how do students ground their understanding about the phenomenon when they learn and solve problems with computer models? Second, what are common sources of mistakes in students’ reasoning with computer models? Results show that students ground their understanding in computer models in five ways: direct observation, straight abstraction, generalisation, conceptualisation, and extension. Students also incorporate into their reasoning their knowledge and experiences that extend beyond phenomena represented in the models, such as attitudes about unsustainable carbon emission rates, human agency, external events, and the nature of computational models. The most common difficulties of the students relate to seeing the modelled scientific phenomenon and connecting results from the observations with other experiences and understandings about the phenomenon in the outside world. An important contribution of this study is the constructed coding scheme for establishing different ways of grounding, which helps to understand some challenges that students encounter when they learn about complex phenomena with agent-based computer models.

     

Explicitly Teaching for Transfer: Effects on the Mathematical Problem‐Solving Performance of Students with Mathematics Disabilities

The purpose of this study was to explore methods to enhance mathematical problem solving for students with mathematics disabilities (MD). A small‐group problem‐solving tutoring treatment incorporated explicit instruction on problem‐solution rules and on transfer. The transfer component was designed to increase awareness of the connections between novel and familiar problems by broadening the categories by which students group problems requiring the same solution methods and by prompting students to search novel problems for these broad categories. To create a stringent test of efficacy, we incorporated a computer‐assisted practice condition, which provided students with direct practice on real‐world problem‐solving tasks. We randomly assigned 40 students to problem‐solving tutoring, computer‐assisted practice, problem‐solving tutoring plus computer‐assisted practice, or control, and pre‐ and posttested students on three problem‐solving tasks. On story problems and transfer story problems, tutoring (with or without computer‐assisted practice) effected reliably stronger growth compared to control; effects on real‐world problem solving, although moderate to large, were not statistically significant. Computer‐assisted practice added little value beyond tutoring but, alone, yielded moderate effects on two measures.     

Students’ Understanding Is Enhanced Through Molecular Modeling

Integration of molecular modeling into General Chemistry lab encourages students to dually process molecular concepts both verbally and pictorially. When students are tested utilizing questions not previously encountered the dual processing of information can contribute to a transfer to knowledge. General Chemistry students utilized molecular modeling in lab and a comparison of a treatment and nontreatment group during two semesters is presented for a pretest, posttest, and on semester exam questions. The treatment group tested significantly higher than the nontreatment group on both the posttest and semester exam questions related to molecular concepts illustrating that there was a transfer of knowledge.     

Improving primary students’ collaborative problem solving competency in project-based science learning with productive failure instructional design in a seamless learning environment

The paper reports on an empirical study adopting a mixed research method, aiming at improving primary students’ collaborative problem solving competency in project-based learning with productive failure (PF) instructional design in a seamless learning environment. Two Grade Six classes participated in a project-based learning of “Plant Adaptations”. In Class 1 with 27 students, the project-based learning was conducted with PF instructional design; in Class 2 with 26 students, the project-based learning was conducted without PF instructional design. The learning activities spanned across farm, class, home and online spaces supported by mobile devices. Data collection includes various students’ created artifacts in groups in the inquiry process, student reflections, student focus group interviews and pre- and post-domain tests. Both qualitative and quantitative data analysis methods were employed. The research findings show that compared to Class 2, the students in Class 1 gained deeper understanding of conceptual knowledge and produced better group artifacts in collaborative problem-solving quality than those in Class 2; and the students in Class 1 were more positive in facing the challenges in their project-based learning process, and developed a sense of ownership of their learning. The findings imply that PF instructional design is conducive to developing primary students’ collaborative solving competency in science learning in a seamless learning environment.     

Using heuristic worked examples to promote inquiry-based learning

Inquiry learning can be facilitated by having students investigate the domain through a computer simulation and express their acquired understanding in a runnable computer model. This study investigated whether heuristic worked examples can further enhance students' inquiry behaviour, the quality of the models they create, and their domain knowledge. High-school students were offered a simulation of an electrical circuit and a modelling tool. Students in the experimental condition (n = 46) could consult heuristic worked examples that explained what activities were needed and how they should be performed. Students in the control condition (n = 36) did not receive this support. Cross-condition comparisons confirmed that heuristic worked examples improved students' inquiry behaviour and enhanced the quality of their models. However, few students created a model that reflected full understanding of the electrical circuit, and the expected between-group difference in posttest scores failed to appear. Based on these findings, improvements to the design of heuristic worked examples are proposed.     

A research methodology for studying how people think

Although science education intends to help students learn to think, research in this area does not usually use psychological research on how people think. The purpose of this article is to describe one type of research, commonly called information-processing psychology. Its goal is understanding how people think while doing complex tasks. It uses detailed data, usually from individual subjects, and develops precise yet powerful models of human performance, often by using a computer. After describing information-processing research, we illustrate it with two studies. The first shows how computer models are used to explain thinking. A computer program models the knowledge needed to understand and use a physics textbook. The second study shows how information-processing approaches can be used systematically but more simply. This study clarifies why students find it so difficult to master the “factor-label” method for converting chemical units. The article concludes with a discussion of guidelines and suggestions for using information-processing ideas.     

Scaffolding strategies in a rubric-based intervention to promote engineering students’ ability to address wicked problems

ABSTRACT

In recent years, there has been increasing interest within the engineering education research community to prepare engineering students to address wicked problems (WPs) such as climate change, resource scarcity and violent conflict. Previous research suggests that engineering students are able to address WPs if they are given adequate support, but there is a lack of research on what kinds of support are needed. This paper aims to reduce this gap by reporting on students’ performance in, and approaches to, addressing WPs when different scaffolding strategies were used in different parts of a rubric-based intervention. The intervention aimed to provide undergraduate engineering students with an understanding of the nature of WPs and with a structured way of addressing them. For each part of the intervention, we discuss affordances for learning provided by the different scaffolding strategies. The results suggest that strong cognitive scaffolding can support students’ understanding of the nature of WPs and students’ performance in written responses to WPs, but possibly also limits deep engagement with WPs and transfer of learning to other contexts.     

The Impact of Learner’s Prior Knowledge on Their Use of Chemistry Computer Simulations: A Case Study

It is complicated to design a computer simulation that adapts to students with different characteristics. This study documented cases that show how college students’ prior chemistry knowledge level affected their interaction with peers and their approach to solving problems with the use of computer simulations that were designed to learn electrochemistry. Students with different prior knowledge levels were found to use different approaches to solving problems with the use of computer simulations. In particular, the cases showed that students with a high level of prior knowledge tended to use the equations and formulas to accomplish the learning tasks and then use the computer simulations to confirm their predictions. Students with a low level of prior chemistry knowledge used the computer simulations as the main resources to accomplish their tasks. Considerations of individual differences and the integration of learning materials were suggested for further research on instructional use of computer simulations.     

The impact of three‐dimensional computational modeling on student understanding of astronomical concepts: a quantitative analysis

The increased availability of computational modeling software has created opportunities for students to engage in scientific inquiry through constructing computer‐based models of scientific phenomena. However, despite the growing trend of integrating technology into science curricula, educators need to understand what aspects of these technologies promote student learning. This study used a multi‐method research approach involving both quantitative (Paper 1) and qualitative data (Paper 2) to examine student conceptual understanding of astronomical phenomena, relative to two different instructional experiences. Specifically, based on students' understandings of both spatial and declarative knowledge, we compared students who had constructed three‐dimensional computational models with students who had experienced traditional lecture‐based instruction. Quantitative analysis of pre‐interview and post‐interview data revealed that construction of three‐dimensional models best facilitated student understandings of spatially related astronomical concepts — whereas traditional instruction techniques best facilitated student understandings of fact‐oriented astronomical knowledge. This paper is the first in a two‐paper set that continues our line of research into whether problem‐based courses such as the Virtual Solar System course can be used as a viable alternative to traditional lecture‐based astronomy courses.     

Systems View of School Climate: a Theoretical Framework for Research

School climate has been widely examined through both empirical and theoretical means. However, there is little conceptual consensus underlying the landscape of this literature, offering inconsistent guidance for research examining this important construct. In order to best assist the efforts of developing causal models that describe how school climate functions, we propose the Systems View of School Climate (SVSC). This theoretical framework was formed by deconstructing prior models and empirical research on school climate into themes and highlighting their implicit assumptions. Using the SVSC to synthesize this existing literature, school climate is defined as the affective and cognitive perceptions regarding social interactions, relationships, values, and beliefs held by students, teachers, administrators, and staff within a school. School climate is situated within Ecological Systems Theory (Bronfenbrenner 1989) to guide future research in this domain and help specify levels of research or analysis, thereby providing utility as a theoretical framework for future causal models. The SVSC provides a roadmap for research by demarcating school climate from related constructs, suggesting related contextual and structural constructs, and delineating proximal and distal systems which may shape the nature of school climate.     

The Effectiveness of the Geospatial Curriculum Approach on Urban Middle-Level Students’ Climate Change Understandings

Climate change science is a challenging topic for student learning. This quantitative study examined the effectiveness of a geospatial curriculum approach to promote climate change science understandings in an urban school district with eighth-grade students and investigated whether teacher- and student-level factors accounted for students’ climate change knowledge achievement. The participants included 12 science teachers and 956 eighth-grade students. Data included a pre- and posttest climate change assessment measures for both teachers and students and a teacher measure of Geospatial Science-Technological Pedagogical Content Knowledge. Paired-sample t tests revealed statistically significant gains from pretest to posttest on their climate change knowledge (p < .001; effect sizes being large on multiple-choice items and medium on the open-ended response assessment). Both ordinary least squares (OLS) multiple regression and 2-level hierarchical linear modeling found that students’ initial climate change knowledge and gender were significant predictors for students’ posttest scores, p < .05. Students’ pretest scores were the strongest significant predictor of the posttest scores, p < .001. Neither the teachers’ climate change knowledge nor their Geospatial Science-Technological Pedagogical Content Knowledge had significant association with the students’ posttest scores. Teaching years was a significant predictor for students’ posttest scores in OLS regression (p < .001). The findings provide support that a geospatial curriculum approach is an effective science curriculum approach for learners in urban middle-level education.     

Enhancing transfer by learning generalized domain knowledge structures

Transferring one’s knowledge in new situations is usually associated with cognitively demanding processes. The paper explores an approach to facilitating transfer of knowledge by explicitly instructing learners in medium-level generalized but yet domain-connected knowledge structures that are applicable to a broader range of tasks in the domain and could be essential in managing the cognitive load associated with transfer. The paper includes a theoretical analysis of the potential role of the generalized domain knowledge in transfer and an experimental study designed to investigate the effectiveness of explicit instruction in a generalized domain knowledge structure (function–process–structure schema) in technical areas. Forty-nine undergraduate university students with low or no prior knowledge in the domain participated in the randomised 2 (schema-based vs. non-schema-based instruction)?×?2 (general-to-specific vs. specific-to-general knowledge sequences) experiment investigating the effects of these two factors on posttest transfer performance and subjective ratings of learning difficulty (interpreted as indicators of cognitive load). The results indicated a significant (p?<?0.05) main effect of schema-based instruction; a possible trend (p?<?0.1) favouring general-to-specific instructional sequence for posttest test performance; and a significant interaction between the two factors for ratings of difficulty. The paper concludes that (a) transfer within a domain could be facilitated by explicitly instructing learners in generalized domain schemas; (b) general-to-specific approach could possibly be used as a preferred instructional sequence for enhancing transfer; and (c) cognitive load perspective could add some valid arguments to explain the role of generalized domain knowledge in transfer.     

The influence of instruction,prior knowledge,and values on climate change risk perception among undergraduates

We evaluated influences on the climate change risk perceptions of undergraduate students in an introductory Earth Science course. For this sample, domain‐specific content knowledge about climate change was a significant predictor of students' risk perception of climate change while cultural worldviews (individualism, hierarchy) and political orientation were not. These results contrast with previous studies highlighting worldviews as a dominant influence on risk perception. At the beginning of the semester, students' climate change content knowledge was relatively low, with average scores on a 21‐item test less than 50%. Post instruction results indicated that students learned climate change science during the course, and their perceptions of risks associated with climate change increased. Unlike most prior research evaluating links between climate change knowledge and risk perception, our measure of content knowledge was a validated assessment specific to climate change. Use of this specific climate knowledge test may be one reason that we detected a relationship between climate knowledge and risk perception whereas most of the previous research has not. Another—possibly complementary—explanation may be a generational shift between our study sample and prior samples. Undergraduates today, having grown up with more exposure to climate change in schools and the media than previous generations, may be diverging from average adults in that learning climate science appears to also increase their perceptions of the risks climate change poses. Undergraduate courses with embedded climate‐related activities present an opportunity to both increase climate science knowledge and risk perceptions of future decision makers.     

Learning residential electrical wiring through computer simulation: The impact of computer‐based learning environments on student achievement and cognitive load

Multimedia learning environments such as computer simulations are widely accepted as tools for supporting science learning. Although the design of multimedia learning environments can be domain specific, few studies have focused on the use of computer simulations for learning residential electrical wiring. This study aimed to determine whether students using computer simulations learned better than traditional classroom learners in the domain of residential wiring. A quasi‐experiment was implemented with 169 high school students. The simulation group participated in a series of computer simulations, whereas the control group received lectures and demonstrations from an instructor. Students' cognitive load as elevated by multimedia leaning tasks was compared with that of students learning using traditional methods. The simulation group learned significantly better and reported higher cognitive load than did the control group. Moreover, the simulation group managed cognitive resources more efficiently on transfer of learning than did the control group. Having more opportunities to interact with a simulation‐based learning environment could result in higher cognitive load. The higher cognitive load seemed to result in better performance on the achievement test and, therefore, the learners' mental effort was possibly invested mainly in meaning making in the virtual learning environments. Discussion of the results, instruments and research design, as well as suggestions for future studies are provided.