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.

     

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.     

Schema abstraction with productive failure and analogical comparison: Learning designs for far across domain transfer

Although there has been considerable research into knowledge transfer for over a century, there remains a need for specific, validated techniques for teaching for transfer. This article reports on classroom-based research in which students learned about complex systems and climate change with agent-based computer models using two different instructional approaches based on productive failure (PF). In both PF approaches, students initially explored a problem space on their own and then received teacher-led instruction. One treatment group used climate computer models whereas the other group engaged in analogical comparisons between the same climate computer models and complexity computer models in different domains. The study found both groups demonstrated significant learning gains by posttest on assessments of declarative and explanatory knowledge and on within domain near transfer. However, students in the two models treatment group performed at a significantly higher level on an across domain far transfer problem solving task. Theoretical and practical implications are considered.     

人工智能教学如何培养高中生的计算思维?——基于人工智能案例驱动的Python编程教学的实证研究

目前利用编程教学培养计算思维已经取得共识,人工智能案例驱动的编程教学解决了单纯的编程教学枯燥乏味、难以激发学生兴趣的难题。依据人工智能案例驱动的编程教学理念,在分析计算思维、Python、人工智能案例三者关系的基础上,提出了人工智能案例驱动的Python编程教学培养高中生计算思维的教学模型。以"有声小说"为人工智能案例驱动的Python编程教学案例,进行了具体的教学设计。研究采用准实验研究法,通过三个月的跟踪观察,辅以作品分析、学习成绩分析和访谈来考察人工智能案例驱动的Python编程教学对学生计算思维的影响。研究表明,人工智能案例驱动的Python编程教学对高中生计算思维的培养具有显著的促进作用。基于研究结果,提出了以下六条实施建议:要厘清教学内容、教学方式及教学目标之间的关系;关注所教知识背后的思维、方法与观念;引导学生对核心概念的理解与迁移;精讲知识并注重能力的培养;关注个体差异并循序渐进;重视情境对于学习的重要性。     

教学效率数学模型建构的教育原理及意义

如何提高教学效率,是各层次各类教学研究的重要问题。在教学理论和实践基础上首次提出的“教学效率的数学模型”,可以直观地理解教学效率的三个限制性原理,即在学校教育中不能仅仅强调学生自主学习,不能仅仅强调教师单向传授知识,教与学是相互“耦合”的。其原理表明三个不可能：只要求学生自主学习,不可能高效;只要求教师灌输,不可能高效;不将教师的教学转化为学生的自学,不可能高效。以此模型推论,对于不同学科、不同水平的教师和学生,两条“学历一效率”曲线的交点0是不同的。这就为因材施教地研究教学效率提供了新思路。在系统研究教学模型基础上建构的“教学模型的数学模型”,是“模式的模式”,即元模式。元模式的研究具有方法论价值,为教师在教学中选择合适的教学模型提供了实用的模式“网络结构”。这些数学模型之于远程教育的意义是：远程教学十分需要新的突破。     

“So,we kind of started from scratch,no pun intended”: What can students learn from designing games?

Much research attention has been focused on learning through game playing. However, very little has been focused on student learning through game making, especially in science. Moreover, none of the studies on learning through making games has presented an account of how students engage in the process of game design in real time. The present study seeks to address that gap. We report an exploratory embedded case study in which three groups of students in one classroom created a computer game designed to teach peers about climate science, while drawing on scientific knowledge, principles of game design, and computational thinking practices. Data sources were student design sheets, computer video, and audio screen capture while students created their game, and interviews after completing the curriculum unit. A theme-driven framework was used to code the data. A curricular emphasis on systems across climate systems, game design, and computational thinking practices provided a context designed to synergistically supported student learning. This embedded case study provides a rich example of what a collaborative game design task in a constructionist context looks like in a middle school science classroom, and how it supports student learning. Game design in a constructionist learning environment that emphasized learning through building a game allowed students to choose their pathways through the learning experience and resulted in learning for all despite various levels of programming experience. Our findings suggest that game design may be a promising context for supporting student learning in STEM disciplines.     

研究生招生计划编制原则及其影响因素分析

科学地分配研究生招生计划能够有效促进学科发展,调节教育资源在学科专业之间的分配。研究生招生计划编制过程中必须遵循目标一致性、均衡性、效益性等原则,并充分考虑政策、学科师资等影响因素。运用层次分析法设计研究生招生计划编制模型是完全可行的,分配结果也较为合理。分配模型中各因素权重的设置较为困难,可采用对多位专家的判断进行信息合成的方法来解决。     

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.     

职业技术院校思想政治课教法探索

“事必有法,然后可成”,在职业学校政治课教学中,改进教学方法,重视导人语,可激发学生学习热情;采用启发式教学,培养学生的创造性思维;理论联系实际,教书育人相结合;应用讨论教学法,充分发挥学生集体智慧。创设情景,寓教于乐,开展自我教育。教无定法,在教学工作中只有遵循科学的教学原则,运用正确的教学方法,才会取得良好的教学效果。     

Toward a computational model of tutoring

This article addresses several issues around successful integration of instructional science and computer science. It addresses issues of building computational models of tutoring and incorporating instructional principles into them. The first barrier to overcome in working toward this integration is development of principled programs in which cognitive principles about learning and teaching are realized at a level of granularity consistent with building computational models. Such cognitive studies would facilitate fine-grained modeling of learning and teaching. The second barrier to overcome is the gap between the two disciplines in terms of goals, motivations, literature, and even definitions of concepts. This situation suggests that effort should be concentrated on two areas: research on understanding basic principles behind learning and teaching, and the establishment of clearer lines of communication between instructional and computer scientists. This article addresses both these issues. An earlier version of this article will appear as a chapter in the NATO Workshop volumeFoundations and Frontiers in Instructional Computing Systems, P. Winne and M. Jones, Eds., Springer-Verlag, NY.     

Aerobasics — An introduction to aeronautics

Modern aircraft are designed for the utmost in performance and safety as the penalty for any shortfalls in these areas is very heavy. Thus they incorporate many complex systems and subsystems, each developed using advanced scientific concepts and manufacturing methods. Aircraft design is undertaken by a group of design engineers, each with a specialized knowledge about a few subsystems but only a rough understanding of the whole aircraft. This rough overall understanding of the concepts and principles based on which an aircraft is organized is a vital element of a design engineer’s toolbox. It helps him perform his own specialized task such that it fits in the complete design seamlessly. This comprehensive knowledge is not easily acquired from modern technical literature which is voluminous and primarily caters to special interest groups. An attempt has been made here to gather this knowledge and present it concisely as a series of articles, so that it will serve as a broad general introduction to aircraft engineering. This material should be useful not only to fresh engineers entering the aeronautical industry but also to students in all relevant branches of engineering. It is also likely to be of general interest to any scientifically inclined reader as a source of basic information in a number of aeronautical disciplines. The general philosophy adopted here is best summarized by the following quote from the Panchatantra: “Knowledge indeed is unlimited, but life is short and there are many obstacles to the acquisition of knowledge. Therefore one must grasp the essential knowledge leaving out much detail ….”     

The American science pipeline: sustaining innovation in a time of economic crisis

Significant limitations have emerged in America's science training pipeline, including inaccessibility, inflexibility, financial limitations, and lack of diversity. We present three effective programs that collectively address these challenges. The programs are grounded in rigorous science and integrate through diverse disciplines across undergraduate, graduate, and postdoctoral students, and resonate with the broader community. We discuss these models in the context of current economic constraints on higher education and the urgent need for our institutions to recruit and retain diverse student populations and sustain the successful American record in scientific education and innovation.     

劳动法公选课教学方法与教学手段探讨

《劳动法》公选课是大学生就业指导体系的重要组成部分,对促进大学生就业起着重要作用。《劳动法》公选课教学方法和教学手段的设计应遵循为教学目标服务、根据授课对象设计、根据教学内容设计等原则。教师必须科学运用教学方法和教学手段来展现教学内容,最终达到使大学生掌握求职就业法律知识的教学目标。     

Knowledge and the university curriculum

What are students learning in university? Can this learning be measured? We do not at this time possess a conceptual framework for understanding what and how knowledge is acquired in different university disciplines. A framework for the acquisition of knowledge would have to account for the manner in which forms of knowledge differ. Disciplinary differences could be expected to occur at four levels: in the nature of the concepts used; in the logical structure of the discipline; in the truth criteria used; and in the methods employed in the discipline. In this article, such a framework is tested on university courses representative of different disciplines. At the most basic level, characteristics of the most important concepts used in courses across disciplines are investigated. The characteristics include concept familiarity, generality and abstractness. At a second level, differences in the logical structure of disciplines are examined through analysis of the relationships between course concepts, the structure of propositions in the field, and organizing principles which play a major role in the discipline. The truth criteria used by various disciplines suggest more global differences which would affect the acquisition of knowledge. Finally, the methods considered important in different disciplines, and their effect on the development of students' intellectual skills, complete the portrayal of the parameters of knowledge and the university curriculum. Directions for future research are discussed.     

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.     

The impact of three‐dimensional computational modeling on student understanding of astronomy concepts: a qualitative analysis

In this study, we explore an alternate mode for teaching and learning the dynamic, three‐dimensional (3D) relationships that are central to understanding astronomical concepts. To this end, we implemented an innovative undergraduate course in which we used inexpensive computer modeling tools. As the second of a two‐paper series, this report focuses on the qualitative differences of students' understandings of both spatial and declarative knowledge domains as reflected by their two distinct learning environments — a traditional astronomy classroom and an experimental astronomy course grounded in problem‐solving and modeling with dynamic, 3D, computational modeling software. We found that students who constructed 3D computational models tended to have a more scientifically sophisticated understanding of dynamic spatial relationships, whereas students in the traditional class developed more accurate understandings of the properties and general facts and figures regarding celestial bodies.     

Integrating Computational Science Tools into a Thermodynamics Course

Computational tools and methods have permeated multiple science and engineering disciplines, because they enable scientists and engineers to process large amounts of data, represent abstract phenomena, and to model and simulate complex concepts. In order to prepare future engineers with the ability to use computational tools in the context of their disciplines, some universities have started to integrate these tools within core courses. This paper evaluates the effect of introducing three computational modules within a thermodynamics course on student disciplinary learning and self-beliefs about computation. The results suggest that using worked examples paired to computer simulations to implement these modules have a positive effect on (1) student disciplinary learning, (2) student perceived ability to do scientific computing, and (3) student perceived ability to do computer programming. These effects were identified regardless of the students’ prior experiences with computer programming.