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Related Concept Videos

Cognitive Learning01:21

Cognitive Learning

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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
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Learning Disabilities01:25

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Learning disabilities are cognitive disorders caused by neurological impairments that affect cognitive functions like language and reading, without indicating overall intellectual or developmental challenges. These disabilities differ from global intellectual or developmental disabilities as they are limited to distinct cognitive functions. Common learning disabilities include dysgraphia, dyslexia, and dyscalculia, each of which impacts unique aspects of learning.
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E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
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Introduction to Learning01:18

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Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
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Community Based Intervention01:30

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Community-based interventions in mental health represent a paradigm shift from institution-centered care to treatments embedded within the fabric of local communities. By prioritizing inclusion and leveraging existing societal structures, this approach fosters a supportive environment conducive to addressing mental health challenges while promoting individual dignity and agency.
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The t-test is a statistical method used to compare the sample mean with a population mean or compare two means from two data sets. The test statistic is calculated from the standard deviation, mean, and number of measurements in the data set at a selected confidence interval and then compared to a table of critical values at this confidence level. If the test statistic is smaller than the critical value, the null hypothesis is accepted. In this case, we state that the difference between the...
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Dataset of mobile learning effectiveness on learning Computer Programming in Community College.

Hon-Sun Chiu1

  • 1Hong Kong Community College, The Hong Kong Polytechnic University, Hong Kong.

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Mobile learning effectiveness was evaluated using Computer Programming subject data from 1434 students. Analysis of subject performance and student surveys provides insights into optimizing mobile learning strategies.

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Area of Science:

  • Educational Technology
  • Computer Science Education

Background:

  • Mobile learning (m-learning) feasibility is established, but its effectiveness remains debated due to conflicting research findings.
  • Technical competence is a critical factor for successful mobile learning implementation.
  • Computer Programming is a foundational subject for technical fields and a compulsory course in Hong Kong Community College.

Purpose of the Study:

  • To present a dataset on mobile learning effectiveness in Computer Programming.
  • To evaluate the overall subject performance of students across different mobile learning environments.
  • To assess students' mobile learning experience through surveys.

Main Methods:

  • Analysis of subject performance data for 1434 students across three cohorts (2015-2017).
  • Statistical analysis using one-way ANOVA with Turkey HSD post-hoc test.
  • Evaluation of student experience via a 5-point Likert scale questionnaire.

Main Results:

  • The dataset includes comprehensive student performance and survey data.
  • Statistical analysis was performed to determine the impact of different mobile learning settings.
  • Student feedback on their mobile learning experience was collected.

Conclusions:

  • The presented dataset offers valuable information for researchers and educators.
  • Insights into effective mobile learning implementation and pedagogical strategies can be derived.
  • Further understanding of mobile learning's impact on technical subject performance is provided.