E-Learning Design:

Chinese Pinyin Initials

People who are familiar with Chinese pinyin letters may not be aware of the pinyin categories. Therefore, the primary goal of this e-learning system is to educate Chinese speakers/learners about the classification of pinyin initials. The development of this e-learning system followed the principles of the Revised Bloom's Taxonomy Table (RBT) and incorporated e-learning principles such as the multimedia principle, the contiguity principle, and the segmenting principle. To assess the effectiveness of the system, an independent sample t-test was conducted, along with an analysis of effect size, to compare the grades obtained in the pretest and post-test stages.

ROLE
UX/UI Designer
Usability Researcher
Website Developer

TOOLS
Pen and Paper (Sketching)
Sketch (Wireframes)
Axure RP (E-Learning System)
T-Test Calculator
Effect Size Calulator

PLATFORM
Desktop

Description

Goals

  • Provide users with a comprehensive understanding of the categories and pronunciation theory of pinyin initials within a 30-40 minute e-learning session.


Intended Users

  • This e-learning module is designed for users who are already familiar with all of the pinyin initials and seek to gain a deeper understanding of the categorization of initials.

Learning Objectives

  • Objective 1: Familiarize learners with the seven initial categories in Chinese pinyin.

  • Objective 2: Help learners memorize all the initials associated with each category.

  • Objective 3: Enable learners to articulate the distinctive pronunciation characteristics of each category.

Table 1.  Learning Objects in English and in Bloom’s Taxonomy With Explanation (Includes Cognitive Process Dimension and Knowledge Dimension)

Table 2. Learning Objects with Matched Knowledge Dimension

Implementation

Sketches & Wireframes

E-Learning System Tools

  • Axure RP: Axure RP serves as the main platform for the e-learning system. After conducting extensive research on e-learning system services, Axure RP was determined to be the most suitable choice based on factors such as cost-effectiveness, layout customization options, and availability for conducting user tests. However, it is worth noting that Axure RP does not support audio integration.

  • Microsoft Forms: Microsoft Forms is utilized for the administration of pretests and post-tests. One of the key advantages of using Microsoft Forms is its ability to automatically calculate grades once users complete the tests, simplifying the evaluation process.

Application of E-Learning System Principles

  • Multimedia Principle: Use both words and graphs in the lectures to enhance learning through multiple channels.

  • Contiguity Principle: Align graphs with corresponding words to promote better understanding and cognitive association.

  • Redundancy Principle: Explain graphs primarily using concise textual descriptions to avoid redundancy.

  • Segmenting Principles: Divide the lesson into seven sections for better retention and comprehension.

  • Practice: Include two practice questions for each section to reinforce learning and assess understanding.

Video 1. E-Learning System Demonstration

Evaluation

Usability Test

  • In the usability test, participants were asked to complete a pretest and post-test to evaluate their learning outcomes. The test included both single-choice and multiple-choice questions. The pretest and post-test had identical questions, but the sequence was randomized to minimize order effects.

    The questions covered topics such as identifying the correct articulation position for a category (OL1 & OL3) and recognizing initials under a category (OL1 & OL2). A total of 12 participants took part in the test, with 7 participants recruited from the surrounding community and 5 participants from the DePaul University participants pool. All participants were at least 18 years old, ensuring they met the age requirement for participation.

Statistical Analysis

  • A t-test was performed to compare the scores obtained in the pretest and post-test. The results of the t-test revealed a significant difference between the two sets of scores (t = -3.29, p < .05). Specifically, the post-test scores (M = 73.33, SD = 26.05) were significantly higher than the pretest scores (M = 35, SD = 30.90). The effect size for this analysis was found to be larger than 0.5, indicating a substantial impact of the intervention on the participants' learning outcomes.