Guided discovery learning with computer-based simulation games: Effects of adaptive and non-adaptive instructional support

Research output: Journal contributionsJournal articlesResearchpeer-review

Authors

The instructional effectiveness of exploring computer-based simulation games is hypothesized to be low unless teaching functions are implemented. According to Klauer's (1985) framework for a theory of teaching, two varieties of instructional support were investigated in three experiments: (1) System-initiated adaptive advice and (2) learner-requested non-adaptive background information. Advice increased verbal domain knowledge, but decreased game performance (N = 64 Grade 7 students) - an effect replicated with university students (N = 38); advice had short-term effects, background information had long-term effects (N = 80 Grades 7-8 students). ATI-effects were observed. The results are discussed concerning instructional implications and cognitive theories of problem-solving, intelligence, memory overload and depth of information processing.

Original languageEnglish
JournalLearning and Instruction
Volume3
Issue number2
Pages (from-to)113-132
Number of pages20
ISSN0959-4752
DOIs
Publication statusPublished - 1993
Externally publishedYes

Recently viewed

Publications

  1. Machine Learning and Knowledge Discovery in Databases
  2. Integration of laser scanning and projection speckle pattern for advanced pipeline monitoring
  3. Derivative approximation using a discrete dynamic system
  4. Towards improved dispatching rules for complex shop floor scenarios - A genetic programming approach
  5. Emergency detection based on probabilistic modeling in AAL-environments
  6. Modeling Conditional Dependencies in Multiagent Trajectories
  7. An Orthogonal Wavelet Denoising Algorithm for Surface Images of Atomic Force Microscopy
  8. Enabling Road Condition Monitoring with an on-board Vehicle Sensor Setup
  9. Fixed-term Contracts and Wages Revisited Using Linked Employer-Employee Data from Germany
  10. Building a process layer for business applications using the blackboard pattern
  11. Analyzing User Journey Data In Digital Health: Predicting Dropout From A Digital CBT-I Intervention
  12. Probabilistic approach to modelling of recession curves
  13. Identification of structure-biodegradability relationships for ionic liquids - clustering of a dataset based on structural similarity
  14. Efficient and accurate ℓ p-norm multiple kernel learning
  15. Building Assistance Systems using Distributed Knowledge Representations
  16. Binary Random Nets I
  17. Cognitive Predictors of Child Second Language Comprehension and Syntactic Learning
  18. AGDISTIS - Graph-based disambiguation of named entities using linked data
  19. Model inversion using fuzzy neural network with boosting of the solution
  20. Trait correlation network analysis identifies biomass allocation traits and stem specific length as hub traits in herbaceous perennial plants
  21. Supporting the Decision of the Order Processing Strategy by Using Logistic Models
  22. Using transition management concepts for the evaluation of intersecting policy domains ('grand challenges')
  23. Partitioned beta diversity patterns of plants across sharp and distinct boundaries of quartz habitat islands
  24. Visualizing the Hidden Activity of Artificial Neural Networks
  25. Clustering Hydrological Homogeneous Regions and Neural Network Based Index Flood Estimation for Ungauged Catchments
  26. Global temporal typing patterns in foreign language writing
  27. Implementing ERP systems in multinational projects
  28. Efficient Order Picking Methods in Robotic Mobile Fulfillment Systems
  29. Mathematics in Robot Control for Theoretical and Applied Problems
  30. Linux-based Embedded System for Wavelet Denoising and Monitoring of sEMG Signals using an Axiomatic Seminorm
  31. Sequencing and fading worked examples and collaboration scripts to foster mathematical argumentation - working memory capacity matters for fading
  32. Multi-Parallel Sending Coils for Movable Receivers in Inductive Charging Systems
  33. Data-Driven flood detection using neural networks