Instructional design

Summary

Instructional design (ID), also known as instructional systems design (ISD), is the practice of systematically designing, developing and delivering instructional materials and experiences, both digital and physical, in a consistent and reliable fashion toward an efficient, effective, appealing, engaging and inspiring acquisition of knowledge. The process consists broadly of determining the state and needs of the learner, defining the end goal of instruction, and creating some "intervention" to assist in the transition. The outcome of this instruction may be directly observable and scientifically measured or completely hidden and assumed. There are many instructional design models but many are based on the ADDIE model with the five phases: analysis, design, development, implementation, and evaluation. Robert M. Gagné is considered one of the founders of ISD due to the great influence his work, The Conditions of Learning, has had on the discipline. History Origins A

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https://en.wikipedia.org/wiki/Instructional_design

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The effects of practice schedule and critical thinking prompts on learning and transfer of a complex judgment task.

Many instructional strategies that appear to improve learners' performance during training may not realize adequate posttest performance or transfer to a job. The converse has been found to be true as well: Instructional strategies that appear to slow the learner's progress during training often lead to better posttraining or transfer performance. For example, many studies have shown beneficial effects of random over blocked practice on transfer of learning, even though blocked practice often leads to better performance during the training session. In a 2 × 3 factorial experiment (N = 120), with the factors practice schedule (random, blocked) and critical thinking prompts (before task, after task, none), this study investigates whether this also applies to complex judgment tasks and whether critical thinking prompts can enhance the effectiveness of particular practice schedules. It is hypothesized that prompts provided after task execution yield best transfer in a random practice schedule, whereas prompts provided before task execution yield best transfer in a blocked schedule. In line with this hypothesis, a blocked schedule led to better performance than random practice during training but not on the transfer test, where a random schedule was beneficial. The hypothesized interaction effect was also found: Critical thinking prompts after task execution significantly benefit transfer performance of participants following a random schedule, and transfer performance following a blocked schedule can be a little enhanced through providing critical thinking prompts before task execution. These results warrant instruction in critical thinking processes to teach complex judgment tasks, using random practice schedules combined with critical thinking prompts provided after task execution.

2011

Intelligent tutoring systems adapt the curriculum to the needs of the individual student. Therefore, an accurate representation and prediction of student knowledge is essential. Bayesian Knowledge Tracing (BKT) is a popular approach for student modeling. The structure of BKT models, however, makes it impossible to represent the hierarchy and relationships between the different skills of a learning domain. Dynamic Bayesian networks (DBN) on the other hand are able to represent multiple skills jointly within one model. In this work, we suggest the use of DBNs for student modeling. We introduce a constrained optimization algorithm for parameter learning of such models. We extensively evaluate and interpret the prediction accuracy of our approach on five large-scale data sets of different learning domains such as mathematics, spelling learning, and physics. We furthermore provide comparisons to previous student modeling approaches and analyze the influence of the different student modeling techniques on instructional policies. We demonstrate that our approach outperforms previous techniques in prediction accuracy on unseen data across all learning domains and yields meaningful instructional policies.

2017

The adaptivity of intelligent tutoring systems relies on the accuracy of the student model and the design of the instructional policy. Recently an instructional policy has been presented that is compatible with all common student models. In this work we present the next step towards a universal instructional policy. We introduce a new policy that is applicable to an even wider range of student models including DBNs modeling skill topologies and forgetting. We theoretically and empirically compare our policy to previous policies. Using synthetic and real world data sets we show that our policy can effectively handle wheel-spinning students as well as forgetting across a wide range of student models.

Association for Computing Machinery2016