Norbert M. Seel

Computer-Based Diagnostics and Systematic Analysis of Knowledge

What is knowledge? How can it be successfully assessed? How can we best use the results? As questions such as these continue to be discussed and the learning sciences continue to deal with expanding amounts of data, the challenge of applying theory to diagnostic methods takes on more complexity. Computer-Based Diagnostics and Systematic Analysis of Knowledge meets this challenge head-on as an international panel of experts reviews current and emerging assessment methodologies in the psychological and educational arenas. Emphasizing utility, effectiveness, and ease of interpretation, contributors critically discuss practical innovations and intriguing possibilities (including mental representations, automated knowledge visualization, modeling, and computer-based feedback) across fields ranging from mathematics education to medicine. These contents themselves model the steps of systematic inquiry, from theoretical construct to real-world application: Historical and theoretical foundations for the investigation of knowledge Current opportunities for understanding knowledge empirically Strategies for the aggregation and classification of knowledge Tools and methods for comparison and empirical testing Data interfaces between knowledge assessment tools Guidance in applying research results to particular fields Researchers and professionals in education psychology, instructional technology, computer science, and linguistics will find Computer-Based Diagnostics and Systematic Analysis of Knowledge a stimulating guide to a complex present and a rapidly evolving future.

What is Instructional Design

Instructional Design (ID) is commonly defined as a systematic procedure in which educational and training programs are developed and composed aiming at a substantial improvement of learning (e.g., Reiser & Dempsey, 2007).

Mental Models in Learning Situations

Abstract Learning situations where phenomena are explained require the construction and successful manipulation of mental models. In these situations the models have the function to facilitate simplifcation and visualization of the modeled phenomena and the construction of analogies. This chapter reprots recent developments in research on model-centered learning with a focus on design-based modeling in the context of exploratory learning and guided discovery learning. Results of two large empirical studies on mental models in multimedia learning and discovery learning are reported.

General Didactics and Instructional Design: eyes like twins A transatlantic dialogue about similarities and differences, about the past and the future of two sciences of learning and teaching.

Although General Didactics (GD) and Instructional Design (ID) have not shown many points of contact in the past, there are some obvious parellels from the perspective of their historical development. This will be examined in detail in this article. More specifically, we speak about model building, which has characterized General Didactics and Instructional Design for some decades. However, the models of General Didactics and Instructional Design are not problem-free with regard to the continuity and advancement of both disciplines. First, we will describe the historical roots of both disciplines and examine which elements of theory are of central importance. Second, we will try to answer the question of which kind of model building could be considered as predominant and what problems result from this predominance. In order to do this, we will describe empirical studies on the use of instructional models and discuss these studies from the perspective of the philosophy of science. Third, we will draw inferences for future processes of model building in order to prevent the same problems that happened in the past from happening again. Finally, we will discuss the issue of what General Didactics can learn from Instructional Design and vice versa.

Mental Models & Instructional Planning

Important educational implications have been drawn mainly from two movements in epistemology: constructivism and situated cognition. Whereas constructivism is relevant for instruction primarily on a meta-theoretical level, the concept of situated cognition has strong educational implications for instructional practice. A central assumption of situated cognition is that people construct mental models to meet the requirements of (learning) situations to be cognitively mastered. Research on how to influence the construction of mental models has been criticized by several authors from a theoretical and methodological perspective. This chapter asks: Has descriptive research on mental models in instructional contexts provided results that can serve as a foundation for prescriptions to facilitate or improve the student’s construction of mental models? We first discuss the characteristics of learning situations that necessitate the construction of mental models. Our next step is a search for theoretically sound conceptions of instruction that either impel students to construct mental models for themselves or which adaptively guide and direct the students in the process of model construction. We report on an exploratory study which investigated: (a) the applicability of cognitive apprenticeship for designing effective learning environments; (b) the effect of providing an initial conceptual model on learner construction of mental models during instruction; and, (c) the long-term effectiveness of a multimedia learning program on acquired domain-specific knowledge and the stability of initially constructed mental models. Finally, we address what happens when there are no relevant learner preconceptions available.

Model-Based Learning and Performance

Model-based learning is both a new and old paradigm of psychology and education. In pedagogy we can find this idea since decades (and until today various conceptions of model-based learning have been developed in the fields of mathematics, physics or geography education aiming at guided discovery and exploratory learning. Traditionally, there are two major approaches of theory and research on model-based learning: A functional-pragmatic approach and a constructivist approach, which is closely related with the theory of mental models. This chapter focuses on both approaches with a particular emphasis on measuring the effects of model-based learning on different performance criteria, such as understanding and problem solving, analogical reasoning, and situation-dependent decision making.

Effects of Creative Dispositions on the Design of Lessons

Since Skinners distinction between the science of learning and the art of teaching, it is an unanswered question whether the ability to teach and to plan lessons is based on learning by advice or on special dispositions of the teachers. We therefore addressed the question whether a creative disposition of instructional design students has effects on their designs of lessons. We conducted a study with 44 students. Every student had to design two different lessons which were analyzed in regard to their creativity. Creative lesson designs were defined as innovative being practicable at the same time, and as structurally varied. The results do not provide clear evidence that more creative persons are able to design more innovative lessons that are practicable at the same time. Nevertheless an in-depth analysis shows that more creative participants design more varied lessons.

The Role of Supportive Information in the Development and Progression of Mental Models

In learning a complex skill, creation and elaboration of learners’ conceptual and causal models benefit from supportive information provided at the beginning of instruction. On the other hand, it has been documented that practicing problem solving leads to better performance and transfer of complex cognitive skills. Despite the essential role of problem-solving practice for integration and transfer of knowledge and skills, providing novice learners with supportive information before practice can contribute substantially to the progression of a learner’s mental model toward an expert-like mental model. This progression process was examined before and after three phases of the instructional process: supportive information presentation, problem-solving practice, and test performance. Participants’ mental models of the complex learning task were matched against an expert mental model at five observation points through an instructional troubleshooting session. The results indicated a significant change in participants’ mental models after receiving the supportive information and no change after practice or performance.

Essentials of Computer-Based Diagnostics of Learning and Cognition

As a result of the rapid progress of computer technology in recent decades, researchers from different areas have adopted artificial intelligence to develop computer-aided instruction systems and diagnostic tools for the assessment of learning and cognition. Referring to the central questions on “What is knowledge?” and “How can we assess knowledge?” this introductory chapter will focus on some essentials of computer-based diagnostics of knowledge and cognition. First, some basic ideas of educational diagnostics and diagnoses are described, resulting in a distinction between “responsive” and “constructive” approaches of knowledge assessment. In the subsequent sections, computer-based procedures are described with regard to both approaches. They presuppose the application of external representations grounded on the semantics of natural language. The next section of this introduction focuses on computer-based and agent-based methodologies of knowledge diagnosis as a central component of automatic diagnostic systems. The final section will provide a brief preview of the major topics of this volume.

Methodik der Erziehungswissenschaft

In diesem Kapitel befassen wir uns mit den methodischen Grundlagen der Erziehungswissenschaft. Die wissenschaftstheoretischen Grundlagen der Erziehungswissenschaft wurden bereits in Abschn. 1.3.1 behandelt. Wir empfehlen Ihnen, sich diese noch einmal zu vergegenwartigen, bevor Sie mit der Lekture dieses Kapitels beginnen.