Addressing the Challenges of Virtual Learning on High School Students

Joshua Yoo

Purpose

• With COVID-19 causing schools to go virtual, students faced new challenges through virtual learning
• The questionnaire was made and given to students to find the effects of virtual learning at MLWGS

Four Constructs

• Four constructs were made for this research
1. Big Five Personality

a. Extroversion, Agreeableness, Emotional Instability, Consciousness, Intellect

• Information Technology

a. Performance Expectancy, Effort Expectancy, Hedonic Motivation

• Psychological Learning Processes

a. Learning Orientation, Self Regulated Learning Strategies, Absorptive Capacity

• Learning Outcomes
1. GPA Change, Satisfaction, Continuous Intention
• Control Variables
• Age, Gender, Experience, Device

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Sources of Questionnaire

Basic Facts

• A total of 250 responses were collected and used
• 81 males, 166 females and 3 non-binary took the survey
• 83 freshman, 58 sophomores, 57 juniors and 52 seniors took the survey

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Personality

• Five personality types were surveyed in the context of Virtual Learning

Control Variables

Effects of Virtual Learning

Effects Sorted by Grade

Effects Sorted by Gender

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Regressions

• Using R, regressions were deployed to find the direct and interactions of three constructs, (i.e., Big Five Personality, Information Technology and Psychological Learning Process) on the three dependent variables (i.e., GPA Increase, Satisfaction and Continuous Intention).

GPA Increase

• Personality, Information Technology and Psychological Learning Processes are significant on GPA increase.

Note: * Significant at α < .05

** Significant α < .01

*** Significant α < .001

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Satisfaction

• Information Technology and Psychological Learning Processes are significant for Satisfaction.

Note: * Significant at α < .05

** Significant α < .01

*** Significant α < .001

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Continuous Intention

• Personality and Information Technology are Significant for Continuous Outcomes.

Note: * Significant at α < .05

** Significant α < .01

*** Significant α < .001

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Findings

• The personality that had the most significance was extroversion in continuous intention. People who were more extroverted were less likely to use virtual learning in the future
• Information Technology (Performance Expectancy, Effort Expectancy, Hedonic Motivation) was significant for all Learning Outcomes (GPA Increase, Satisfaction, Continuous Intention)

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Machine Learning

Using R

Support Vector Machine

• Using the R programming language, machine learning was used to find the support vector machine (SVM), finding which variables were the most significant for the response variables of GPA Increase, Continuous Intention and Satisfaction and the accuracy of each. The best SVM was radial basis.

SVM Radial: GPA Increase

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Accuracy: 88%

SVM Radial: Satisfaction

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Accuracy: 85%

SVM Radial: Continuous Intention

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Accuracy: 79%

Full Tree Models

• Using the R programming language, Full Tree Models were created to see which independent variables and their values would change the response variables.

Full Tree: GPA Increase

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Accuracy: 80%

Full Tree: Continuous Intention

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Accuracy: 70%

Full Tree: Satisfaction

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Accuracy: 86%

Key

• EXT_M – Extroversion
• AGB_M– Agreeableness
• EMI_M– Emotional Instability
• COS _M– Consciousness
• INT_M – Intellect
• PFE_M – Performance Expectancy
• EFE_M – Effort Expectancy
• FCC_M – Facilitation Conditions
• HDM_M – Hedonic Motivation
• DEV_M – Device

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• SRL_M – Self-Regulated Learning Strategies in Virtual Learning
• LOR_M – Learning Orientation for Virtual Learning
• ABC_M – Absorptive Capacity
• GPA_M – GPA Change
• SAT_M – Satisfaction
• CTI_M – Continuous Intention
• GEN_M – Gender
• GRD _M – Grade
• ASN_M – Asynchronous experience
• SYN_M – Synchronous experience

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