Learner preferences and prior knowledge in learner-controlled computer-based instruction

Kopcha, Theodore J.; Sullivan, Howard J.

doi:10.1007/s11423-007-9058-1

Cited by 52 publications

(34 citation statements)

References 34 publications

(65 reference statements)

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“…Ten items adapted from Copcha and Sullivan (2008) were used to measure control perceptions during week 8 (e.g., "In this class, I could decide how much time to spend practicing problems" and "In this class, I could decide in which order to study different chapters"), rated on a scale from 1 ¼ strongly disagree to 6 ¼ strongly agree. The items reflect learners' autonomy perceptions of how, when and in which order they complete coursework (Cronbach's alpha ¼ .92).…”

Section: Methodsmentioning

confidence: 99%

Frequent deadlines: Evaluating the effect of learner control on healthcare executives' performance in online learning

Fulton

Ivanitskaya

Bastian

et al. 2013

Learning and Instruction

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Section: Methodsmentioning

confidence: 99%

Frequent deadlines: Evaluating the effect of learner control on healthcare executives' performance in online learning

Fulton

Ivanitskaya

Bastian

et al. 2013

Learning and Instruction

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“…Rieber (1990) found that students learning Newton's Laws of Physics scored higher on tests after using an interactive animation (an animation followed by multiple-choice questions including feedback). In addition, Kopcha and Sullivan (2008) have found that high-prior-knowledge students achieved better when their preference for interactivity was matched with the type of program they used. There is one assumption underlying all of these interactivity theories: the higher the interactive level, the better the product.…”

Section: Proactive Interactionmentioning

confidence: 99%

The impact of animation interactivity on novices’ learning of introductory statistics

Wang

Vaughn

Liu

2011

Computers & Education

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and their never-ending supports. This study was a one-way design where the independent variable was animation interactivity. In addition to a control group (Static Group) provided with only static materials, there were three groups with different levels of animation interactivity: 1) Animation with simple interactivity (Simple Animation Group), 2) animation with input vii manipulation (Input Group), and 3) animation with practice and feedback (Practice Group). A sample of 123 college students participated in the study and was randomly assigned into groups. They gathered in the computer lab to work with the animation program and then took online surveys and tests for evaluation. Students were expected to learn Principles of Hypothesis Testing (concepts of type I error, type II error and pvalue). The data collected in this study included 1) student learning attitudes, 2) achievement and confidence pre-test scores, 3) achievement and confidence post-test scores, and 4) program perception. Also, student manipulation of the animation program was recorded as Web log data. The data were analyzed by using multivariate analysis (MANOVA), univariate analysis (ANOVA), regression analysis, regression tree analysis and case analysis.The findings were as follows: 1) Animation interactivity impacted students' improvement in understanding (p=.006) and lower-level applying (p=.042), 2) animation interactivity did not impact student confidence and program perception, 3) the regression analysis indicated that student prior knowledge and interest were the most important predictors on student achievement post-test scores instead of program manipulation, and 4) the regression tree showed that there were interactions among student interest, prior knowledge, and program manipulation on the achievement post-test scores. The case analysis showed that not all students manipulated the interactive animation program as expected due to a lack of motivation and cognitive skills, and this could decrease the effect of the interactive animation. This study hoped to broaden theories on interactive learning and serve as a reference for future statistics curriculum designers and textbook publishers.viii He asked students to first clarify the definition of the type I and type II error, and then look for clues in the question for computation. The type I and type II error problems are difficult for most novice learners. But with the concrete examples of "bags," "boxes," List of Tablesor "envelopes," statistics was no longer as difficult for me. This particular teaching method is valuable for use in all introductory-level classes. 3I believe this struggle with learning statistics is not limited to myself, but is common to many other students. As a result, I created an online animated program based on my instructor's teaching method in order to help learners who also struggle with learning type I error, type II error and the p-value. This online animation was created to popularize this particular teaching method. To benefit learners with different ...

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“…With regard to learning, learners may not develop motives for choosing between tasks that differ in their structural features and thus be unable to distinguish between tasks that are necessary for learning and tasks that could just as well be omitted (Ross & Morrison, 1989). This negatively influences the learning process (Kopcha & Sullivan, 2008;Tennyson & Buttery, 1980;Williams, 1996) and even enlarges individual differences between low and high ability learners (Merrill, 2002;Snow, 1980). With regard to motivation, learners who are not aware of structural differences between tasks will probably not see the reason for choosing between these tasks.…”

Section: Structural Task Featuresmentioning

confidence: 99%

Dynamic task selection: Effects of feedback and learner control on efficiency and motivation

Corbalan

Kester

Merriënboer

2009

Learning and Instruction

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Structural features of learning tasks are relevant for problem solving but not salient for novice learners. Feedback in the form of Knowledge of Correct Response (KCR) during practice is expected to help learners recognize the structural features and to profit from learner control over the selection of learning tasks. A 2 x 2 factorial experiment (N = 118) was conducted to study the effects of the KCR feedback (present, absent) and control over the selection of learning tasks (learner control, program control). The presence of the KCR feedback yielded higher efficiency on a near transfer test as well as higher learner motivation. An interaction between feedback and control, indicating extra beneficial effects of feedback when learners control the selection of learning tasks, was not found. Theoretical and practical implications are discussed.

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Learner preferences and prior knowledge in learner-controlled computer-based instruction

Cited by 52 publications

References 34 publications

Frequent deadlines: Evaluating the effect of learner control on healthcare executives' performance in online learning

Frequent deadlines: Evaluating the effect of learner control on healthcare executives' performance in online learning

The impact of animation interactivity on novices’ learning of introductory statistics

Dynamic task selection: Effects of feedback and learner control on efficiency and motivation

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