Patterns of Verbal Mediation During Problem Solving: A Sequential Analysis of Self-Explanation

The explanations that participants give themselves (self-explanations) while learning or solving problems have been shown to be positively associated with various performance measures. The major aim of this study was to identify patterns of self-explanation that distinguish between good and poor problem solvers. Thirty-two Grade 9 students were asked to solve 3 mixture problems—1 warm-up problem and 2 test problems—while thinking aloud. The problem-solving process was videotaped, protocols were transcribed, and 5 content categories were identified. Through a sequential analysis, patterns of self-explanation that differentiate between good and poor problem solvers were identified.     

Coordinating principles and examples through analogy and self-explanation

Research on expertise suggests that a critical aspect of expert understanding is knowledge of the relations between domain principles and problem features. We investigated two instructional pathways hypothesized to facilitate students’ learning of these relations when studying worked examples. The first path is through self-explaining how worked examples instantiate domain principles and the second is through analogical comparison of worked examples. We compared both of these pathways to a third instructional path where students read worked examples and solved practice problems. Students in an introductory physics class were randomly assigned to one of three worked example conditions (reading, self-explanation, or analogy) when learning about rotational kinematics and then completed a set of problem solving and conceptual tests that measured near, intermediate, and far transfer. Students in the reading and self-explanation groups performed better than the analogy group on near transfer problems solved during the learning activities. However, this problem solving advantage was short lived as all three groups performed similarly on two intermediate transfer problems given at test. On the far transfer test, the self-explanation and analogy groups performed better than the reading group. These results are consistent with the idea that self-explanation and analogical comparison can facilitate conceptual learning without decrements to problem solving skills relative to a more traditional type of instruction in a classroom setting.     

Testing the instructional fit hypothesis: the case of self-explanation prompts

Cognitive science principles should have implications for the design of effective learning environments. The self-explanation principle was chosen for the current work because it has developed significantly over the last 20 years. Early formulations hypothesized that self-explanation facilitated inference generation to supply missing information about a concept or target skill, whereas later work hypothesized that self-explanation facilitated mental-model revision (Chi, Handbook of research on conceptual change, 2000). To better understand the complex relationship between prior knowledge, cognitive processing, and changes to a learner’s representation, two classes of self-explanation prompts (gap-filling and mental-model revision) were tested in the domain of physics problem solving. Prompts designed to focus the learner on gap-filling led to greater learning and a reduction in the amount of tutoring assistance required to solve physics problems. The results are interpreted as support for the instructional fit hypothesis—the idea that the efficacy of instruction is contingent on the match between the cognitive processing that the instruction elicits, how those processes modify the underlying knowledge representations for the task, and the utility of those representations for the task or problem.     

Understanding problem solving behavior of 6–8 graders in a debugging game

Debugging is an over-looked component in K-12 computational thinking education. Few K-12 programming environments are designed to teach debugging, and most debugging research were conducted on college-aged students. In this paper, we presented debugging exercises to 6th–8th grade students and analyzed their problem solving behaviors in a programming game – BOTS. Apart from the perspective of prior literature, we identified student behaviors in relation to problem solving stages, and correlated these behaviors with student prior programming experience and performance. We found that in our programming game, debugging required deeper understanding than writing new codes. We also found that problem solving behaviors were significantly correlated with students’ self-explanation quality, number of code edits, and prior programming experience. This study increased our understanding of younger students’ problem solving behavior, and provided actionable suggestions to the future design of debugging exercises in BOTS and similar environments.     

Lessening the Load of Misconceptions: Design-Based Principles for Algebra Learning

The current study examined the effectiveness of self-explanation prompts, visual signaling cues, and a combination of the two features on middle school students’ (N = 202) algebra learning. Also explored were the differential effects of features for students with faulty conceptual knowledge (evidenced by a higher prevalence of making errors during problem solving) on learning. That is, we assessed whether students who make prevalent conceptual errors predictive of algebra performance differentially benefit from design features. Participants were randomly assigned to complete 1 of 4 sets of worked example assignments supplemented with self-explanation prompts (n = 51), visual signaling cues (n = 49), both features (n = 51), or neither feature (n = 51). Worked examples supplemented with either self-explanation prompts or signaling cues led to greater learning from pre- to posttest in comparison to the worked example control, with practically meaningful effects. The effect of assignments supplemented with signaling cues was moderated by error prevalence. Those who made errors more frequently demonstrating misunderstanding of algebraic concepts (e.g., the meaning of a coefficient) benefited significantly more from visual signaling cues alone than self-explanation prompts alone or control assignments. These findings highlight the importance of considering differential effects of design features when used in combination or in isolation, particularly for struggling students.     

Finding the Best Types of Guidance for Constructing Self-Explanations of Subgoals in Programming

Subgoal learning, a technique used to break down problem solving into manageable pieces, has been used to promote retention and transfer in procedural domains, such as programming. The primary method of learning subgoals has been passive, and passive learning methods are typically less effective than constructive methods. To promote constructive methods of learning subgoals, we prompted learners to self-explain the subgoals of a problem-solving procedure. Self-explanation asks learners to make sense of new information based on prior knowledge and logical reasoning. Novices’ self-explanation is typically more effective when they receive guidance, because it helps them to focus on relevant information. In the present experimental study, the types of guidance that students received while self-explaining determined whether the constructive learning method was more effective than the passive method. Participants assigned to the constructive learning method performed best when they either received hints about the subgoals or received correct explanations as feedback, but not when they received both. These findings suggest that constructive learning of subgoals can further improve the benefits of subgoal learning when students receive only guidance that complements their construction of knowledge. This nuance is important for educators who engage their students in constructive learning and self-explanation.     

Learning From Examples: Fostering Self-Explanations in Computer-Based Learning Environments

Cognitive skills acquisition involves developing the ability to solve problems in knowledge-rich task domains, and is particularly important for any individual attempting to meet the challenges of our modern, knowledge-driven economy. This type of economy argues for reconceptualizing cognitive skills acquisition as a lifelong process. Research has shown that worked-out examples are the key to initial cognitive skill acquisition and, therefore, critical to lifelong learning. The extent to which learners' profit from the study of examples, however, depends on how well they explain the solutions of the examples to themselves. This paper discusses our own research on different types of computer-based learning environments that indirectly foster self-explanations by (a) fostering anticipative reasoning, (b) supporting self-explanations during the transition from example study to problem solving, and (c) supporting self-explanation activities with instructional explanations. It also discusses ways of leveraging new computer and video technologies to enhance these environments by representing problem situations and their concepts dynamically. The paper concludes by suggesting that these learning environments, if employed successfully, can encourage systematic, lifelong learning.     

Assisting self-explanation prompts are more effective than open prompts when learning with multiple representations

Learning with multiple representations is usually employed in order to foster understanding. However, it also imposes high demands on the learners and often does not lead to the expected results, especially because the learners do not integrate the different representations. Thus, it is necessary to support the learners’ self-explanation activity, which concerns the integration and understanding of multiple representations. In the present experiment, we employed multi-representational worked-out examples and tested the effects of two types of self-explanation prompts as help procedures for integrating and understanding multiple representations. The participants (N = 62) learned about probability theory under three conditions: (a) open self-explanation prompts, (b) self-explanation prompts in an assistance-giving-assistance-withholding procedure (assisting self-explanation prompts), or (c) no prompts (control group). Both types of self-explanation prompts fostered procedural knowledge. This effect was mediated by self-explanations directed to domain principles. Conceptual knowledge was particularly fostered by assisting self-explanation prompts which was mediated by self-explanations on the rationale of a principle. Thus, for enhancing high-quality self-explanations and both procedural knowledge and conceptual understanding, we conclude that assisting self-explanation prompts should be provided. We call this the assisting self-explanation prompt effect which refers to the elicitation of high-quality self-explanations and the acquisition of deep understanding.     

Exploring the effects of integrating self-explanation into a multi-user game on the acquisition of scientific concepts

The purpose of this study was to examine the impacts of embedding collaboration into a game with a self-explanation design for supporting the acquisition of light and shadow concepts. The participants were 184 fourth graders who were randomly assigned to three conditions: a solitary mode of the game with self-explanation, a collaborative mode with self-explanation, or the control condition of a single-user game without integrating self-explanation. Students' conceptual understanding was measured through an immediate posttest and a retention test with a three-week delay. Further, students' engagement in answering the prompts was also investigated. The findings showed that having students collaboratively play science-based games with a self-explanation design embedded was not sufficient to help them learn the science concepts. Rather, it was the level of engagement in responding to the self-explanation prompts that mattered.     

Promoting transfer: effects of self-explanation and direct instruction

Explaining new ideas to oneself can promote transfer, but how and when such self-explanation is effective is unclear. This study evaluated whether self-explanation leads to lasting improvements in transfer success and whether it is more effective in combination with direct instruction or invention. Third- through fifth-grade children (ages 8-11; n=85) learned about mathematical equivalence under one of four conditions varying in (a) instruction on versus invention of a procedure and (b) self-explanation versus no explanation. Both self-explanation and instruction helped children learn and remember a correct procedure, and self-explanation promoted transfer regardless of instructional condition. Neither manipulation promoted greater improvements on an independent measure of conceptual knowledge. Microgenetic analyses provided insights into potential mechanisms underlying these effects.     

基于问题驱动的语文阅读教学

课程改革是为了促进学生的发展,实现课程改革中知识本位向学生本位的转变。在教学活动中,教师的教学方式和学生的学习方法改革尤其重要。基于问题的语文阅读教学,其目的在于以学生的阅读学习为问题设计的出发点,为学生呈现真实的、有意义的情境,培养学生的探究学习能力,有助于学生发展思维,成为独立、自主的学习者。     

自我解释的研究述评

自我解释是指学习者向自身做出解释,以理解新信息的一种认知活动。研究表明,在学习过程中,自我解释的学习者对学习材料的理解更好,这种现象称为“自我解释效应”。文章从自我解释效应的影响因素,促进自我解释和自我解释的心理机制三个角度出发,回顾了以往关于自我解释的研究。最后,提出了三个有待进一步研究的问题。     

Examining the effects of combining self-explanation principles with an educational game on learning science concepts

Educational researchers have indicated that although computer games have the potential to promote students’ motivation and engagement, the work on how to design effective games that fulfil educational purposes is still in its infancy. This study aimed to examine how integration of self-explanation into a computer game affected primary schoolers’ acquisition of light and shadow concepts. The participants were randomly assigned to either an experimental group or a control group and played a computer game with or without self-explanation prompts individually as a treatment. Students’ conceptual understanding was evaluated through a pretest and a posttest administered right after the treatment. The results revealed that by controlling the pretest scores, students who played the game with self-explanation features did not outperform those who played the game without any prompts in the posttest. Further analyses of the experimental group students’ responses to the self-explanation prompts also indicated that the students with more correct responses to the prompts did not perform better than those with lower accuracy rates. The deficits in the use of self-explanation prompts are identified, and possible improvements to enhance the function of self-explanation in educational games are proposed.     

对自我解释研究的分析

自我解释是学生在学习过程中自己向自己解释学习内容的知识获得过程。该文解释了“自我解释”的含义、总结了自我解释的类型、比较了几种自我解释产生的认知机制、归纳了影响因素,从而提出了关于自我解释需要进一步研究的未来问题。     

“自我解释”的类型、影响因素及产生机制

自我解释是一种由自我产生并指向自我的知识建构活动。领域性知识、加工方式、引导角色及激励都会影响自我解释的发生。其产生机制主要有三种模型：空缺填补理论认为自我解释是学生发现自己的知识漏洞并对这些漏洞进行弥补的过程；产生推论及修复心理模型的双加工机制认为自我解释是对不完整文本进行推论和对不完整的心理模型进行修复的过程；拓展情境模式通过比较前两种模式，认为自我解释是修复个人已有的、存在缺陷的领域，在不替代原有心理模型的基础上加速该模型的改变。     

Exploring problem conceptualization and performance in STEM problem solving contexts

Problem solving abilities are critical components of contemporary Science, Technology, Engineering and Mathematics (STEM) education. Research in the area of problem solving has uncovered much about the representation, processes and heuristic approaches to problem solving. However, critics claim this overemphasis on the process of solving problems has led to a dearth in understanding of the earlier stages such as problem conceptualization. This paper aims to address some of these concerns by exploring the area of problem conceptualization and the underlying cognitive mechanisms that may play a supporting role in reasoning success. Participants (N?=?12) were prescribed a series of convergent problem-solving tasks representative of those used for developmental purposes in STEM education. During the problem-solving episodes, cognitive data were gathered by means of an electroencephalographic headset and used to investigate students’ cognitive approaches to conceptualizing the tasks. In addition, interpretive qualitative data in the form of post-task interviews and problem solutions were collected and analyzed. Overall findings indicated a significant reliance on memory during the conceptualization of the convergent problem-solving tasks. In addition, visuospatial cognitive processes were found to support the conceptualization of convergent problem-solving tasks. Visuospatial cognitive processes facilitated students during the conceptualization of convergent problems by allowing access to differential semantic content in long-term memory.

     

样例学习中自我解释效应的研究

在样例学习过程中,学习者对样例的知识内容进行自我解释,会导致样例学习成绩提高,这就是自我解释效应。Chi等人最初对自我解释效应所进行的研究发现,自我解释效应的影响因素包括先行知识、一般能力和个体差异。对自我解释效应的三种理论解释是:间隙—填充(gap—filling)、图式—结构(schema formation)和相似增强(analogical enhancement)。未来对自我解释效应研究的关注点是:自我解释效应的机制、人为设定自我解释提示能否改进学习效果,以及建构性活动与样例学习效果之间的关系。     

基于解释的协作知识建构过程模型

文章从解释的视角,把协作知识建构看作是学习者在不断进行自我解释和与他人交互解释中建构知识的过程。文章详细描述了协作知识建构过程中各类知识间的转化和传播,并对此过程中知识形态的转化、成员间知识的产生和获取进行了深入分析,最后从计算机支持的角度建立了基于解释的协作知识建构过程模型。     

“Accepting Emotional Complexity”: A Socio-Constructivist Perspective on the Role of Emotions in the Mathematics Classroom

A socio-constructivist account of learning and emotions stresses the situatedness of every learning activity and points to the close interactions between cognitive, conative and affective factors in students’ learning and problem solving. Emotions are perceived as being constituted by the dynamic interplay of cognitive, physiological, and motivational processes in a specific context. Understanding the role of emotions in the mathematics classroom then implies understanding the nature of these situated processes and the way they relate to students’ problem-solving behaviour. We will present data from a multiple-case study of 16 students out of 4 different junior high classes that aimed to investigate students’ emotional processes when solving a mathematical problem in their classrooms. After identifying the different emotions and analyzing their relations to motivational and cognitive processes, the relation with students’ mathematics-related beliefs will be examined. We will specifically use Frank’s case to illustrate how the use of a thoughtful combination of a variety of different research instruments enabled us to gather insightful data on the role of emotions in mathematical problem solving.