Relationship of Student Participation and Punctuality in Their

Performance in E-Learning Sessions in the Current COVID-19

Context

Ernesto Hernández-Martínez

, Zury Sócola

, Luz Atoche

Vicente Amirpasha Tirado Kulieva

, Lucia Pantoja-Tirado

and Walter Hernández

Universidad Nacional de Jaén, Jaén 06801, Peru

Universidad Nacional de Frontera, Sullana 20103, Peru

Universidad Nacional Autónoma de Tayacaja Daniel Hernández Morillo, Tayacaja 09156, Peru

Notaría Hernández, Jr. Ricardo Palma 680, Bagua 01621, Peru

walter@notariahernandez.com.pe

Keywords: Tardiness, Final Grade, Synchronous Virtual Teaching, Food Industry Engineering, Food Technology Course.

Abstract: The attitudes of the students of the Food Technology course of the Faculty of Food Industry Engineering of

the National University of Frontier were evaluated (Universidad Nacional de Frontera, 2021). This study

conducted during the current health crisis by COVID- 19 covered all 32 synchronous virtual classes of the

2020-I academic semester. It was determined that, although internal motivation is not considered in the

development of the subject, it does influence the final grade. It was hypothesized that the independent variable

"Cognitive behavior", considered as the number of participations minus the number of tardies, which were

grouped in three levels: positive, neutral or negative, influences the dependent variable "Student performance"

whose results were grouped in two levels: pass or fail. According to the results obtained with the chi-square

test with a contingency value of 0.02 lower than 0.05, it was determined that the students's.

1 INTRODUCTION

The transition of the educational era to a completely

digital system due to the current global health crisis

generated by COVID-19 (IESALC, 2020) has

produced the need for evaluations of teaching and

learning through synchronous virtual sessions using

various video call services, being Google Meet one of

the most common, allowing, in addition, follow up on

student participation and attendance.

To facilitate the visualization of these

synchronous sessions, but asynchronously, they are

recorded on platforms such as Moodle, however, this

technique was not considered in this study.

https://orcid.org/0000-0003-3839-3244

https://orcid.org/0000-0002-5814-6497

https://orcid.org/0000-0002-2901-2326

https://orcid.org/0000-0001-8534-9153

https://orcid.org/0000-0001-9157-6088

https://orcid.org/0000-0001-9204-3814

The "Cognitive behavior" of the students,

considered as the independent variable, is an indicator

obtained from the difference between the number of

student participations (Alqahtani and Rajkhan, 2020)

and tardiness (Davis, 2006) during the development

of the subject, acquired through the record registry in

Excel during each session and whose levels of the

variable were: positive, neutral or negative. The

participations were evaluated by means of the

interpretative reading methodology applied by the

teacher based on lessons of 15 to 20 minutes, in

addition to the interventions in each learning session,

but in a smaller proportion. On the other hand,

unpunctuality was evaluated by noting the absence of

students up to 15 minutes after the class had started.

Hernández-Martínez, E., Sócola, Z., Atoche, L., Kulieva, V., Pantoja-Tirado, L. and Hernández, W.

Relationship of Student Participation and Punctuality in Their Performance in E-Learning Sessions in the Current COVID-19 Context.

DOI: 10.5220/0011825100003612

In Proceedings of the 3rd International Symposium on Automation, Information and Computing (ISAIC 2022), pages 27-30

ISBN: 978-989-758-622-4; ISSN: 2975-9463

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

The "Student performance" established as the

dependent variable (Perveen, 2016) and whose two

levels were: pass and fail, was obtained from the

evaluation of the two partial exams, two graded

practices and a formative research work during the

development of the two units of the semester.

Adding the previous contributions to the

consideration that participation and punctuality are

not evaluated in the course, but it is intended to

demonstrate that they do represent a cognitive

influence for the determination of the performance.

2 METHODOLOGY

2.1 Type of Research

Applied, given that during the 32 sessions all students

were evaluated in an auxiliary excel register. During

all the sessions of the semesters, the independent

variable was evaluated, forming its result.

2.2 Hypothesis

 Null hypothesis (Ho): Cognitive behavior does

not significantly influence student performance

on the subject evaluation.

 Alternative hypothesis (Ha): Cognitive

behavior significantly influences student

performance on the subject evaluation.

2.3 Experimental Group

Seventy-six students of the Food Technology course

of the Faculty of Food Industry Engineering of the

National University of Frontier, Sullana, Piura, Peru,

during the 2020-I virtual academic semester.

2.4 Variables

2.4.1 Independent Variable: Cognitive

Behavior

The independent variable was obtained through the

difference between student participation in class

(considering voluntary readings during the teacher's

interpretive reading methodology and significant

interventions during class) and tardiness, considering

a tolerance of 15 minutes after the start of class.

Student tardiness was counted before the end of the

class, in order to eliminate absent students to avoid

possible errors and to ensure the accuracy of the

values. The operationalization of the cognitive

behavior (CB) is detailed in Table 1.

Table 1: Levels of the independent variable cognitive

behavior.

Level of variable Decision criterion

Positive CB>0

Neutral CB=0

ative CB<0

2.4.2 Dependent Variable: Student

Performance

The two levels of this dependent variable were: pass

or fail. The value can range from 0 to 20, and a student

is considered to have passed if his or her final grade

is greater than or equal to 10.5, and if it is lower, he

or she is considered to have failed the course. Table 2

shows the levels of the dependent variable.

Table 2: Levels of the dependent variable student

performance.

Level of variable Decision criterion

Pass Values greater than or

ual to 10.5 u

to 20

Fail Values less than 10.5 to 0

2.5 Experimental Procedure

Using a cross-check table, the students were counted

numerically and as a percentage, classifying the

independent variable “Cognitive behavior” and its

levels: positive, neutral and negative, as well as the

dependent variable “Student performance” with its

levels: pass and fail. In this sense, there are six

different response options.

2.6 Data Analysis

In order to verify that between the two variables under

study, which are cognitive behavior and student

performance, there is a significant relationship at the

95% confidence level, a chi-square test was

performed (Kuehl, 2001).

3 RESULTS

About the evaluated results shown in Table 3, we

have that in the first column are groups 1 and 2 that

correspond to two groups (morning and afternoon) of

the Food Technology I subject and while those in

group 3 correspond to Food Technology II.

Among the three groups there is a sample of

seventy-six students, in which the calculation of the

difference between participation and tardiness is

shown in order to determine the cognitive behavior,

ISAIC 2022 - International Symposium on Automation, Information and Computing

as well as the grades obtained corresponding to

student performance.

Table 3: Values obtained for cognitive behavior and student

performance.

T.G. S. P. T. C.B. S.P.

-5 10.0

1 2 2 4 -2 10.8

1 3 2 0 2 8.8

1 4 1 3 -2 11.4

1 5 2 7 -5 11.8

1 6 2 7 -5 8.2

1 7 1 0 1 13.4

1 8 2 3 -1 11.6

1 9 1 0 1 14.3

1 10 2 1 1 12.7

1 11 3 1 2 12.7

1 12 1 1 0 7.8

1 13 3 0 3 12.3

1 14 0 2 -2 1.8

1 15 0 7 -7 7.9

1 16 1 2 -1 11.8

1 17 1 6 -5 8.1

1 18 1 3 -2 9.6

1 19 3 0 3 12.6

1 20 1 1 0 9.7

1 21 2 1 1 11.2

1 22 5 1 4 10.1

1 23 0 4 -4 9.5

1 24 1 2 -1 9.3

1 25 3 4 -1 13.2

1 26 2 0 2 12.2

1 27 1 1 0 10.9

1 28 0 3 -3 10.5

1 29 1 0 1 8.1

1 30 2 4 -2 10.4

1 31 0 0 0 13.6

1 32 5 0 5 17.7

1 33 1 3 -2 8.5

1 34 1 4 -3 9.7

2 35 2 5 -3 9.7

2 36 6 1 5 12.9

2 37 0 2 -2 10.7

2 38 3 7 -4 8.6

2 39 0 2 -2 11.8

2 40 8 3 5 14.0

2 41 3 2 1 11.5

2 42 5 1 4 11.2

2 43 6 5 1 11.2

2 44 3 4 -1 10.4

2 45 1 8 -7 10.5

2 46 2 0 2 10.3

2 47 2 2 0 11.4

2 48 3 1 2 10.3

2 49 6 0 6 10.6

2 50 5 0 5 11.5

2 51 1 2 -1 12.1

3 52 0 0 0 10.7

3 53 2 4 -2 9.9

3 54 7 0 7 15.6

3 55 0 1 -1 11.9

3 56 1 5 -4 7.3

3 57 0 2 -2 9.9

3 58 3 1 2 11.2

3 59 2 3 -1 10.5

3 60 0 1 -1 6.8

3 61 2 4 -2 8.3

3 62 4 1 3 17.6

3 63 0 0 0 10.6

3 64 0 0 0 9.0

3 65 3 4 -1 10.7

3 66 3 7 -4 9.4

3 67 0 0 0 12.3

3 68 0 7 -7 8.0

3 69 0 1 -1 11.7

3 70 0 0 0 7.6

3 71 1 0 1 13.5

3 72 0 0 0 10.5

3 73 6 1 5 10.6

3 74 0 10 -10 9.7

3 75 0 4 -4 8.3

3 76 0 1 -1 10.8

Note: T.G. (Technology Group), S. (Student), P.

(Participation), T. (Tardiness), C.B. (Cognitive

Behavior), S.P. (Student Performance).

After interpreting the data shown in Table 3, the

data were grouped in a double-entry table according

to the levels of the variables. The results are shown in

Table 4.

Table 4: Double-entry results on students' final grades and

cognitive behavior.

Cognitive

Behavio

Student

Performance

Total

Level Pass Faile

ative Count 14 24 38

Total 18.4% 31.6% 50.0%

Neutral Count 7 5 12

Total 9.2% 6.6% 15.8%

Positive Count 21 5 26

Total 27.6% 6.6% 34.2%

Total Count 42 34 76

Total 55.3% 44.7% 100%

The Table 4 shows that in the negative assessment

the majority obtained a "failed" grade (31.6%), in the

positive assessment the majority obtained a "passed"

grade (27.6%) and the neutral attitudinal assessment

(0) out of 12 students, 7 obtained a "passed" grade and

5 obtained a "disapproved" grade.

Relationship of Student Participation and Punctuality in Their Performance in E-Learning Sessions in the Current COVID-19 Context

4 DISCUSSION

Table 5 shows the results of the Chi-square test, used

to determine if there is a significant relationship

between the independent and dependent variable.

As shown in Table 5, since the asymptotic

significance value of the obtained Chi-square of

0.002 is lower than the established significance level

of 0.05, it is established that the independent variable

"Cognitive behavior" significantly influences the

dependent variable "Student performance".

Likewise, since the asymptotic significance value

of the contingency coefficient (0.002) is also lower

than the significance level (0.05), it is ratified that

there is a significant relationship between the two

variables mentioned.

Table 5: Chi-square test.

Origin Value Degrees of

freedmon

Sig. asymp-

totic (2

sides)

Pearson's

Chi s

uare

12.103 2 0.002

Plausibility

ratio

12.741 2 0.002

Contingency

Coefficient

0.371 0.002

No. of valid

cases

5 CONCLUSIONS

The validity of the alternative hypothesis was

demonstrated, which proposed the existence of a

significant relationship between the independent

variable "Cognitive behavior" of the students and the

dependent variable "Students' performance" during

the development of the subject.

According to the results obtained through the chi-

square test, it was determined that, during

synchronous virtual teaching in the 2020-I academic

semester, cognitive behavior, represented by

participation and tardiness of students, has a

significant influence on their performance in the

courses evaluated.

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Alqahtani, A. Y., Rajkhan, A. A., 2020. E-Learning Critical

Success Factors during the COVID-19 Pandemic: A

Comprehensive Analysis of E-Learning Managerial

Perspectives. Education Sciences, 10(9), 216.

Davis, M., 2006. Group dynamics in the e-learning context:

online or off track? The East Asian Learner, 2, 1-11.

IESALC, 2020. COVID-19 and higher education: Today

and Tomorrow. Impact Analysis, Policy Responses and

Recommendations. UNESCO.

Perveen, A., 2016. Synchronous and Asynchronous E-

Language Learning: A Case Study of Virtual University

of Pakistan. Open Praxis, 8(1), 21-39.

Kuehl, R., 2001. Design of Experiments, Statistical

Principles of Research Design and Analysis, Thomson

Learning. Mexico, 2

edition.

Universidad Nacional de Frontera, 2021.

http://www.unf.edu.pe/unf/

ISAIC 2022 - International Symposium on Automation, Information and Computing