Example-based learning: The benefits of prompting organization before providing examples

Example-based learning often follows a design in which learners first receive instructional explanations that communicate new principles and concepts and second examples thereof. In this setting, using the knowledge components of the instructional explanations to explain the examples (i.e., generating principle-based self-explanations) is considered to be a highly important learning process. However, a potential suboptimality of this learning process is that it scarcely requires learners to organize the content of the instructional explanations into coherent mental representations. Thus, in two experiments we investigated whether prompting learners to organize the content of the instructional explanations before providing them with the examples (and self-explanation prompts) enhances the effectiveness of example-based learning. We consistently found that organization prompts fostered learning regardless of whether the learners also received self-explanation prompts. Hence, in example-based learning, learners should be prompted to not only generate principle-based self-explanations but also to organize the content of the instructional explanations.     

Learning mathematics from worked-out examples: Analyzing and fostering self-explanations

Recent research has shown that learning from worked-out examples is of major importance for initial skill acquisition in well-structured domains such as mathematics. However, only those learners who actively process the presented examples profit noticeably from this learning mode. Specifically, the learning outcomes depend on how well the learners explain the solution steps presented in the examples to themselves (‘self-explanation effect”). In a series of studies on learning mathematics from examples, learners’ spontaneous self-explanations and instructional means used to encourage self-explanations were investigated. In this research, the following main findings were obtained. Most learners were rather passive with respect to their spontaneous self-explanations. Among the active and successful learners, two subgroups employing different self-explanation styles could be identified. With regard to the instructional means used to induce effective example processing, it turned out that to employ “learning by teaching” in order to stimulate explanation activities was of very limited use. Attempts to directly train for or elicit certain types of self-explanations were more successful. However, even in the latter case, self-explanations had inherent deficits (e.g., proneness to errors). Thus, we sought to design learning arrangements that try to integrate self-explanations with well-timed and well-adapted instructional explanations (e.g., from tutors) in order to enhance students’ problem-solving skills.     

Can learning from molar and modular worked examples be enhanced by providing instructional explanations and prompting self-explanations?

In two experiments we explored how learning from traditional molar worked-out examples—focusing on problem categories and their associated overall solution procedures—as well as from more efficient modular worked-out examples—where intrinsic cognitive load is reduced by breaking down complex solutions into smaller meaningful solution elements—can be further enhanced. Instructional explanations or self-explanation prompts were administered to increase germane cognitive load. However, both interventions were not effective for learning and prompting for self-explanations even impaired learning with modular examples. In the latter case, prompting might have forced learners to process redundant information, which they had already sufficiently understood.     

How Effective are Instructional Explanations in Example-Based Learning? A Meta-Analytic Review

The worked example effect within cognitive load theory is a very well-established finding. The concrete effectiveness of worked examples in a learning situation, however, heavily depends on further moderating factors. For example, if learners improve their processing of worked examples by actively explaining the worked examples to themselves, they are usually better able to solve transfer problems. Another way to enhance example processing is to present learners with instructional explanations instead of prompting them to produce these explanations on their own. In this article, we review 21 experimental studies to address the issue whether instructional explanations support example-based learning. Meta-analytic results lead to three important conclusions: First, the benefits of instructional explanations for example-based learning per se are minimal. Second, instructional explanations are more helpful for acquiring conceptual knowledge than for acquiring procedural knowledge. Third, instructional explanations are not necessarily more effective than other methods supporting example processing such as self-explaining.     

数字化游戏的叙事性交互设计

情境认知理论认为知识是人与情境、人与社会交互的产物,学习者学习的意义建构是通过与"认知情境"的交互实现的,而基于情境认知理论设计的数字化游戏采用了叙事性交互来实现学习者与游戏故事的互动,叙事性交互是一种新的游戏元素。文章探讨了叙事性要素的概念和设计策略,以及实现的方法。     

Example-Based Learning: Integrating Cognitive and Social-Cognitive Research Perspectives

Example-based learning has been studied from different perspectives. Cognitive research has mainly focused on worked examples, which typically provide students with a written worked-out didactical solution to a problem to study. Social-cognitive research has mostly focused on modeling examples, which provide students the opportunity to observe an adult or a peer model performing the task. The model can behave didactically or naturally, and the observation can take place face to face, on video, as a screen recording of the model's computer screen, or as an animation. This article reviews the contributions of the research on both types of example-based learning on questions such as why example-based learning is effective, for what kinds of tasks and learners it is effective, and how examples should be designed and delivered to students to optimize learning. This will show both the commonalities and the differences in research on example-based learning conducted from both perspectives and might inspire the identification of new research questions.     

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.     

Developing Personalized Education: A Dynamic Framework

Personalized education—the systematic adaptation of instruction to individual learners—has been a long-striven goal. We review research on personalized education that has been conducted in the laboratory, in the classroom, and in digital learning environments. Across all learning environments, we find that personalization is most successful when relevant learner characteristics are measured repeatedly during the learning process and when these data are used to adapt instruction in a systematic way. Building on these observations, we propose a novel, dynamic framework of personalization that conceptualizes learners as dynamic entities that change during and in interaction with the instructional process. As these dynamics manifest on different timescales, so do the opportunities for instructional adaptations—ranging from setting appropriate learning goals at the macroscale to reacting to affective-motivational fluctuations at the microscale. We argue that instructional design needs to take these dynamics into account in order to adapt to a specific learner at a specific point in time. Finally, we provide some examples of successful, dynamic adaptations and discuss future directions that arise from a dynamic conceptualization of personalization.

     

Seeking optimal confusion: a review on epistemic emotion management in interactive digital learning environments

Emotions play a significant part in students’ learning experiences within complex educational environments. However, the impact of emotional experiences on effective learning is not straightforward. For example, being confused during learning may be perceived as an adverse event. There is, however, considerable research evidence suggesting that confusion can also be a productive aspect of a student's learning processes. Despite this, research also suggests that when confusion is persistent it can become harmful, promoting learner frustration or boredom. Key challenges for the design of interactive digital learning environments (IDLEs) are to detect and assess students’ emotions and to tailor the environment accordingly. In this paper we examine, from a review of the literature, the implications of learners’ confusion that can occur in IDLEs. Strategies for managing students’ confusion will then be discussed and examples of features enhancing learning in IDLEs will be suggested.     

Encountering the expertise reversal effect with a computer-based environment on electrical circuit analysis

This study examined the effectiveness of a computer-based environment employing three example-based instructional procedures (example–problem, problem–example, and fading) to teach series and parallel electrical circuit analysis to learners classified by two levels of prior knowledge (low and high). Although no differences between the instructional procedures were observed, low prior knowledge learners benefited most from traditional example–problem pairs while their high prior knowledge counterparts benefited most from problem–example pairs. Overall, this study provides empirical support for the expertise reversal effect, which suggests that the effectiveness of certain instruction procedures in example-based learning environments depends upon the learners' level of prior knowledge.     

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.     

Designing a constructive learning activity with interactive elements: the effects of perspective-shifting and the quality of source material

When teachers or instructors create computer-based learning environments, they often solely consider technical aspects of interactivity. As a consequence, learners’ main role is to respond to requests of the learning environment (e.g. by answering multiple-choice questions). This aspect of interactivity is, however, not sufficient to understand the complex benefits of interactivity for learners’ knowledge acquisition. In order to create a higher level of interactivity, an instructional task that encourages learners to design learning materials for fellow learners is used in this paper. We will show that this instructional task can induce interactive elements because learners are encouraged to take not only their own perspective into account when designing. In addition, we investigated if the quality of source material affects knowledge acquisition in design tasks. In a two-by-two design, students (n?=?108) had to design either a learning environment for others (i.e. to perform perspective-shifting), or a representation of acquired knowledge for themselves (no perspective-shifting) with less or more coherent information sources. Results indicate that performing perspective-shifting can be a powerful technique for eliciting interactive learning behavior and, thus, for learning. The quality of information sources does not influence knowledge acquisition to a great extent.     

Designing Instructional Examples to Reduce Intrinsic Cognitive Load: Molar versus Modular Presentation of Solution Procedures

It is usually assumed that successful problemsolving in knowledge-rich domains depends onthe availability of abstract problem-typeschemas whose acquisition can be supported bypresenting students with worked examples.Conventionally designed worked examples oftenfocus on information that is related to themain components of problem-type schemas, namelyon information related to problem-categorymembership, structural task features, andcategory-specific solution procedures. However,studying these examples might be cognitivelydemanding because it requires learners tosimultaneously hold active a substantial amountof information in working memory. In ourresearch, we try to reduce intrinsic cognitiveload in example-based learning by shifting thelevel of presenting and explaining solutionprocedures from a `molar' view – that focuseson problem categories and their associatedoverall solution procedures – to a more`modular' view where complex solutions arebroken down into smaller meaningful solutionelements that can be conveyed separately. Wereview findings from five of our own studiesthat yield evidence for the fact thatprocessing modular examples is associated witha lower degree of intrinsic cognitive load andthus, improves learning.     

When are powerful learning environments effective? The role of learner activities and of students’ conceptions of educational technology

The goal of this chapter is to outline a theoretical and empirical perspective on how learners’ conceptions of educational technology might influence their learning activities and thereby determine the power of computer-based learning environments. Starting with an introduction to the concept of powerful learning environments we outline how recent developments in information and communication technologies might be used to implement these environments technologically. In the next step we refer to several exemplary empirical studies to argue that the power of computer-based learning environments will largely depend on very detailed aspects of the learning activities within these environments. In order to design these environments so that they elicit effective learning activities, it is necessary to analyze the factors that determine learners’ goals and their choices of processing strategies. The focus of this paper is on one of these factors, namely on learners’ instructional conceptions with regard to educational technology and its use in instruction. Up to now, there has been nearly no research conducted within the educational community addressing the issue of how this particular type of instructional conceptions determine learning activities. Therefore, we review several relevant findings from neighboring fields like epistemological beliefs, attitude research, human computer interaction, or cognitive modeling. We use this review to demonstrate that there are numerous findings outside the educational technology community that deserve much more resonance in this community than they currently receive.     

Interactive Teaching

This paper argues for interaction in lectures, interaction which allows learners time to reflect and discuss. This proposition has a staff development dimension both for lecturers in engineering who do not have (or do not value) the skill of interactive lecturing and for those who do, and who wish to find out more. Examples of these techniques are provided in this paper, in the context of construction management. Our intention is to provide practical help for those seeking innovative methods of teaching. We also hope to advance current debates on whether or not interactive lectures are appropriate for engineering subjects in general and for management subjects in particular. The staff developer in this case has observed an apprehension among staff that interactive teaching is more time consuming: does it take longer to plan (than lectures)? Initially, it may do, as the lecturer develops a repertoire of techniques. In this paper, we try to reduce that time by giving an exemplar, saving others from ‘reinventing the wheel’. However, since adapting teaching is a complex process, we show the process of developing a teaching method to suit educational objectives. Finally, we conclude with ‘hints and tips’, based on the literature, for interactive teaching.     

虚实情境融合的交互式外语自主学习模式研究

外语自主学习模式的发展要以现代信息技术为支撑。针对目前外语自主学习在实行上遇到的问题,文章结合情境学习理论,应用虚实融合的增强现实技术构建交互式外语自主学习情境,分析并设计了系统的整体环境架构,阐述了其中采用的关键技术和方法,在此基础上探讨了增强现实虚实交互环境下的新型外语自主学习模式并进行了实例分析。研究表明,此类学习模式能够有效提高学习者在外语学习中的主动性和自主学习能力。