Pablo Pirnay-Dummer

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.

Understanding models for learning and instruction

Foundations and Methodologies of Mental Model Research.- The Fragmented Nature of Learning and Instruction.- Using an Evidence-Based Approach to Assess Mental Models.- Practical Solutions for the Diagnosis of Progressing Mental Models.- Constructing Mental Models during Learning from Science Text.- Progress in the Diagnostics of Mental Models.- Model-Based Theories of Instruction.- Rendezvous with a Quantum of Learning.- Externalizing Mental Models with Mindtools.- Model-Centered Instruction, the Design and the Designer.- Realizing Model-Based Instruction.- Engineering the Model Environment.- Developments in the Design of Instruction.- Bridging a Gap between Psychology and Instructional Practice.- Model-Based Learning Environments.- Model-Centered Learning and Instructional Design.- From Cognitive Theory to Instructional Practice: Technology and the Evolution of Anchored Instruction.- Complex Skills Development for Todays Workforce.

Automated Knowledge Visualization and Assessment

In this chapter we introduce a computer-based and highly automated measurement technique which enables us to analyze even large sets of data within a few seconds. Closely linked to the demand of new approaches for designing and developing up-to-date learning environments is the necessity of enhancing the design and delivery of assessment systems and automated computer-based diagnostics. In many settings, manual and therefore labor-intensive methods have limits. Hence, following a general assessment framework design, we introduce several automated and integrated tools which have helped us in many studies so far. The technologies which we discuss in this chapter aim at the assessment, re-representation, analysis, and comparison of knowledge. The tools were developed independently and then integrated step by step. The possible applications go beyond the structural and semantic analysis and comparison of knowledge. The tools also allow the development of self-assessment technologies which can be used directly by the learners.

Model-based tools for knowledge assessment

This chapter introduces the functions of knowledge representation and presents a critical review of the model-based tools for assessment: Pathfinder, ALA-Reader, jMAP, HIMATT, AKOVIA. For each model-based tool, foundations and applications are discussed. Next, the tools are compared in order to illustrate their advantages and disadvantages, strengths, and limitations. The latter part postulates that there is no easy and no complete way to integrate any of the model-based tools. However, the strength of good research lies in a best possible integration: Multiple perspectives on the same construct are usually needed. Thus, the further development of existing tools as well as of new ones is necessary to explore human knowledge, its change, decision-making, performance, and problem-solving as our understanding of those complex human potentials evolves.

Understanding models for learning and instruction : essays in honor of Norbert M. Seel

Foundations and Methodologies of Mental Model Research.- The Fragmented Nature of Learning and Instruction.- Using an Evidence-Based Approach to Assess Mental Models.- Practical Solutions for the Diagnosis of Progressing Mental Models.- Constructing Mental Models during Learning from Science Text.- Progress in the Diagnostics of Mental Models.- Model-Based Theories of Instruction.- Rendezvous with a Quantum of Learning.- Externalizing Mental Models with Mindtools.- Model-Centered Instruction, the Design and the Designer.- Realizing Model-Based Instruction.- Engineering the Model Environment.- Developments in the Design of Instruction.- Bridging a Gap between Psychology and Instructional Practice.- Model-Based Learning Environments.- Model-Centered Learning and Instructional Design.- From Cognitive Theory to Instructional Practice: Technology and the Evolution of Anchored Instruction.- Complex Skills Development for Todays Workforce.

States and Processes of Learning Communities. Engaging Students in Meaningful Reflection and Learning

The omnipresence of the Internet and high bandwidth connections has brought about the development of powerful software packages called learning management systems (LMS). An LMS integrates the administration and facilitation of online activities and the distribution of learning materials. In this chapter, it is argued that the use of LMS is limited to only a few technological features and that so far it has failed to promote meaningful learning environments. Therefore, two empirical studies were conducted to investigate these concerns. The first study reports the development of a practicable taxonomy for LMS features. The second study extends the taxonomy for discussion forums. This chapter concludes with future perspectives on the application and extension of the introduced practicable taxonomy for LMS.

Text-guided automated self assessment

Writing plays an important role in institutionalized learning environments. However, it has to be monitored in several ways in order to be successful. We developed automated knowledge assessment tools which allow us to produce instant feedback on a text during the writing process in order to promote self-regulated writing skills. The tools may serve as a complement to human tutoring approaches in learning settings in which individual face-to-face coaching is not possible. To generate text feedback, we used natural language oriented knowledge assessment strategies based on mental model theory and graph theory. Different types of text feedback are then automatically created on the basis of graphs and presented to the learner both for reflection and preflection. So far, we have succeeded in implementing the crucial parts of the coaching into computer-based technology as well as developing and implementing both static and dynamic feedback. Hence, a study of a text-guided assessment tool is presented and discussed. Finally, limitations on the volitional level of the toolset will have to be addressed in future studies.

Challenges for Education in a Connected World: Digital Learning, Data Rich Environments, and Computer-Based Assessment—Introduction to the Inaugural Special Issue of Technology, Knowledge and Learning

The new scope of Technology, Knowledge and Learning emphasizes the increased interest on adaptive and personalized digital learning environments. Educational researchers are faced with new research challenges in the beginning of the twenty-first century, especially focusing on digital learning, game-based learning, automated assessment, and learning analytics. In order to provide a source of reference for future submissions to Technology, Knowledge and Learning, the following sections highlight the key themes of the journal.

Model-Based Knowledge Mapping

One of the core tasks of knowledge management is knowledge assessment. Conducting knowledge assessment within organizations is considerably demanding because it is time and cost intensive and the results are often subjective and not very amenable to validation. In this chapter, we present a new theory-driven approach to knowledge management which simplifies this task considerably: model-based knowledge management. Furthermore, we introduce a new and fully automated text-based technology for the assessment of organizational knowledge. Within this technology new computer linguistic techniques and associative networks are used for knowledge mapping. The results of the technological study and our validation study indicate that our methodology is an efficient time- and cost-saving alternative to common knowledge mapping approaches.

Elicitation of knowledge: Intermezzo 1 - To be moved by knowledge: Moving knowledge moves knowledge about knowing

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.