Objectives of the Study

The purpose of this research is to examine the impact of the perceived effectiveness of the Flexible Learning System (FLS) towards the academic performance of Central Mindanao University Students during COVID-19.

Specifically, the paper aims to:

1. Determine the group of respondents in terms of:

a. Gadget used

## # A tibble: 6 × 3
##   Devices primarily used by students for their online classes…¹ count Percentage
##   <chr>                                                         <int>      <dbl>
## 1 Desktop or laptop                                                61      20.5 
## 2 Desktop/ Laptop and Mobile Phone                                126      42.3 
## 3 Desktop/ Laptop, Mobile Phone and Tablet                          3       1.01
## 4 Mobile Phone and Tablet                                           1       0.34
## 5 Mobile phone                                                    105      35.2 
## 6 Tablet                                                            2       0.67
## # ℹ abbreviated name:
## #   ¹`Devices primarily used by students for their online classes.`

b. Internet connectivity

## # A tibble: 1 × 3
##   `Internet connectivity` count Percentage
##   <lgl>                   <int>      <dbl>
## 1 NA                        298        100

c. Year level

## # A tibble: 4 × 3
##   `Year Level` count Percentage
##   <chr>        <int>      <dbl>
## 1 1st Year        96       32.2
## 2 2nd Year        97       32.6
## 3 3rd Year        61       20.5
## 4 4th Year        44       14.8

d. Gender

## # A tibble: 2 × 3
##   Gender count Percentage
##   <chr>  <int>      <dbl>
## 1 Female   224       75.2
## 2 Male      74       24.8

2. Determine the perceived effectiveness of FLS in terms of:

a. Learners’ Dimension

i. Attitude of the learner towards FLS (Provide the mean score of columns J to O)

## [1] 3.016779

## [1] 2.855705

## [1] 2.483221

## [1] 2.765101

## [1] 2.402685

## [1] 2.704698

## 
## Call:
## lm(formula = GWA ~ LDAttitude, data = FLS)
## 
## Coefficients:
## (Intercept)   LDAttitude  
##     1.66034     -0.02908

## 
## Call:
## lm(formula = GWA ~ LDAttitude, data = FLS)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.41418 -0.16639 -0.03046  0.12689  1.11874 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.66034    0.04386  37.856   <2e-16 ***
## LDAttitude  -0.02908    0.01548  -1.878   0.0614 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2249 on 296 degrees of freedom
## Multiple R-squared:  0.01177,    Adjusted R-squared:  0.008435 
## F-statistic: 3.527 on 1 and 296 DF,  p-value: 0.06137

The intercept value of \(1.66034\) means that if the Attitude of the Learners is \(0\), we can expect an average General Weighted Average of \(1.66034\). The slope value of \(-0.02908\) indicates that there is a negative relationship between the General Weighted Average and Attitude of the Learners. This means that as the attitude towards \(FLS(LDAttitude)\) increases by one unit, the GWA tends to decrease by approximately \(0.02908\) units. The p-value of \(0.0614\) indicates that the coefficient for \(LDAttitude\) is not statistically significant at a conventional significance level of \(0.05\). This means that there is weak evidence to reject the null hypothesis, which suggests that the attitude of the learner towards \(FLS(LDAttitude)\) may not have a statistically significant effect on the General Weighted Average \((GWA)\).

### ii. Mental Health (Provide the mean score of Columns P, Q, R, X, and Y)

## [1] 1.942953

## [1] 1.979866

## [1] 2.020134

## [1] 2.828859

## [1] 2.805369

## [1] 3.538255

## 
## Call:
## lm(formula = GWA ~ LDMental, data = MentalHealth)
## 
## Coefficients:
## (Intercept)     LDMental  
##     1.51719      0.01823

## 
## Call:
## lm(formula = GWA ~ LDMental, data = MentalHealth)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.39376 -0.16239 -0.03346  0.13131  1.16718 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.51719    0.07384  20.548   <2e-16 ***
## LDMental     0.01823    0.02054   0.888    0.375    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2259 on 296 degrees of freedom
## Multiple R-squared:  0.002655,   Adjusted R-squared:  -0.0007148 
## F-statistic: 0.7879 on 1 and 296 DF,  p-value: 0.3755

3. Determine the relationship between the perceived effectiveness of FLS towards the academic performance of CMU students.

Dependent Variable: Column “DV”

Independent Variables: Column “AM”, Column “BB”, Column “BQ”, and Column “CF”

## 
## Call:
## lm(formula = GWA ~ Data$LD + Data$ID + Data$CD + Data$DD, data = Data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.41212 -0.16709 -0.04072  0.12679  1.14108 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.708347   0.091216  18.729   <2e-16 ***
## Data$LD     -0.032404   0.033657  -0.963    0.336    
## Data$ID     -0.007099   0.023529  -0.302    0.763    
## Data$CD      0.030347   0.034165   0.888    0.375    
## Data$DD     -0.030337   0.035991  -0.843    0.400    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2263 on 293 degrees of freedom
## Multiple R-squared:  0.009546,   Adjusted R-squared:  -0.003975 
## F-statistic: 0.706 on 4 and 293 DF,  p-value: 0.5884

3.1 Which of the independent variables significantly predicts the dependent variable?

This means that Column \(AM(LD)\) is the most important independent variable in the regression model. It has the highest coefficient value and significantly predicts the dependent variable, Column \(DV\).

3.2 Provide the coefficient of the independent variables.

3.3 Can we conclude that the model is better than a model having the intercept alone?

The results suggest that the model is better than the one with only the intercept. This is because at least one coefficient, \(β\), is significantly different from zero, as shown by a p-value of \(0.5884\). However, none of the selected variables contribute meaningfully to explaining the dependent variable. Therefore, this model should be disregarded entirely, as it does not improve upon the basic approach of merely averaging the dependent variable.

Stat 53 Final Exam Part I

Justin Lian Caballero

2023-12-21