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American Journal of Public Health Research
DOI: 10.12691/ajphr-1-1-3

The Implementation of a Tele-Homecare System with Service Oriented Architecture and HL7 Message Transmission Standard
Ching-Sung Wang

American Journal of Medical and Biological Research
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The Influence of Ethanol on Pyruvate Kinases Activity in Vivo, in Vitro, in Silico
Lelevich Sergey Vladimirovich, Khrustalev Vladislav Victorovich, Barkovsky Eugene Victorovich, Shedogubova Tatyana Aleksandrovna

American Journal of Medical Sciences and Medicine
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Clinical and Histological Features in Wegener Granulomatosis
Li Cavoli Gioacchino, Passantino Rita, Bono Luisa, Tortorici Calogera, Ferrantelli Angelo, Giammarresi Carlo, Rotolo Ugo

American Journal of Infectious Diseases and Microbiology
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The Curse of Lymphatic Filariasis: Would the Continual Use of Diethylcarbamazine Eliminate this Scourge in Papua New Guinea?
Anne Lanham, Lillian Mwanri

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Digital Natives in a Digital Learning Environment: Can Traditional Style Interventions Improve Student Performance?

Jared Cassibba

Mathematics, Edison Learning, Orlando, United States

Abstract: Academic interventions to increase student success are hard to find and are often mostly ineffective, and possibly even detrimental to the student’s success [1,2,3,4,5]. The purpose of this descriptive quasi-experimental quantitative study was to determine if academic interventions in the form of lecture style instruction and paperwork, in place of the digital format that the students are being provided, is beneficial to digital learners. The interventions only increased student progress by 7.69%, which is not a significant impact (p=0.21, p>.05). Additionally, either intervention method resulted in students earning roughly half (.6±0.2) a credit more than if they did not participate in either intervention. However, intervention 2 should still be implemented due to greater number of students that can potentially be positively impacted, over intervention 1.

Keywords: student performance, academic intervention, digital learning environment

Received July 22, 2022; Revised August 24, 2022; Accepted September 05, 2022

Citation: Jared Cassibba, Digital Natives in a Digital Learning Environment: Can Traditional Style Interventions Improve Student Performance?. American Journal of Educational Research, 10 (9). 536-538. January 2022. doi:10.12691/education-10-9-4

1. Introduction

The education system is currently struggling with a trend of low performance. This low performance is categorized by a lack of student growth. The International Student Assessment Survey, which is provided every three years to students around the globe, demonstrates that students in the United States scores have not increased significantly within the past 18 years in the areas of Mathematics, Science, and reading for both male and female students [6]. Student growth is addressed on the level of individual institution through the usage of interventions. These interventions come in a wide variety of types and possibilities. However, it can be difficult to determine what interventions are necessary and how the interventions should be implemented and evaluated. Since every individual student will react differently when the same intervention is applied, it can be said that academic interventions to increase student success are hard to find and are often mostly ineffective, and possibly even detrimental to the student’s success [1,2,3,4,5].

2. Purpose

The purpose of this study was to determine if academic interventions in the form of lecture style instruction and paperwork, in place of the digital format that the students are being provided, is beneficial to digital native students that are not digital learners. A digital native is defined as “a person who was born or has grown up since the use of digital technology became common and so is familiar and comfortable with computers and the internet [7]”. While a digital learner is a student that utilize technology in an effective manner to ensure an ease of knowledge acquisition [8]. The technology that digital learners utilize includes the usage of multiple different devices, as in smart phones, laptops, tablets, and more. Which allows for the learner to engage with high-value, on-demand, and interactive resources/materials and learn on their time and at their own pace [8]. Instead of being in a stand-alone classroom with a single teacher and pre-determined resources/materials with a pre-determined path, or schedule, for learning. A digital learning environment is unstructured self-guided learning, while the traditional learning environment is structured with a pre-determined learning path and timeline.

This study had two hypotheses, accompanied by two research questions. These hypotheses were used to guide the research questions and the focus of the study. Hypothesis 1 stated that a teacher-led intervention or a self-guided intervention will have a significant positive impact on student performance, which was determined by credits completed in a three-month period. Hypothesis 2 stated that the interventions will have a significant positive impact on student performance, which was determined by comparing average credits completed in a three-month period using a T-test. The two research questions that were used to investigate these hypotheses were: 1) Does the teacher-led intervention improve performance more than self-guided intervention, 2) Do the interventions significantly impact student performance.

3. Methodology & Design

The design for this descriptive quantitative study was quasi-experimental. This study was conducted in a second-chance academic institution (SAI). The SAI utilizes a digital hybrid learning environment (LE). The LE consists of self-paced computer-based instruction with teacher/facilitator monitoring. The SAI had a total of 372 students, 224 male and 148 female. Students were selected for the experimental groups based upon the needed coursework for graduation. The students were organized into three control groups and three experimental groups. The breakdown of the control and experimental groups can be found in Table 1.

Table 1. Group Sample Size

Table 1: This tables displays the sample sizes for each experimental group. Group zero served as a reference for no interventions provided.

3.1. Procedure

The descriptive quasi-experimental quantitative study grouped students based upon need of subject for intervention. The subjects that the interventions covered consisted of: Algebra 1, Transition to College Mathematics, Liberal Arts Mathematics I, and Geometry. The students in the control groups received normal instruction provided by the digital platform (ESW); while the students selected for the experimental groups received either standard lecture type instruction with physical paperwork, or the same physical paperwork with course notes without standard lecture style instruction. Each intervention was provided daily for a three-month period. The student performance was measured by the number of credits that was earned during each three-month period. Research question 1 was investigated using descriptive statistics analysis. All data for research question 2 was assessed using a T-test to compare the significance of the interventions impact upon student performance.

Intervention 1 was gathering the students into a stand-alone classroom for a traditional lecture style instruction. All courses were taught by the same instructor, this was done to ensure consistency in teaching method. All instruction followed the gradual release of responsibility method [9]. The gradual release of responsibility method consists of a teacher-led portion, guided practice section, and an independent practice section [9]. The independent work was used to provide students with grades for each lesson. If all independent work was completed with at least a C (70% or higher), the student would receive up to four credits towards graduation. If any independent work was under a C, students were allowed to make corrections to the assignments, for partial credit, and still potentially receive up to four credits towards graduation.

Intervention 2 consisted of students being provided all the classwork that was assigned during intervention 1, along with the notes that were used for instruction in intervention 1. The same notes and assignments from intervention 1, were used in intervention 2 for consistency. Once the students were provided with the assigned work, the students would complete all the assigned work independent of a teacher’s instruction. However, the students did have access to a teacher if they needed further explanation on any specific assigned question, or questions. If all independent work was completed with at least a C, the student would receive up to four credits towards graduation. If any independent work was under a C, students were allowed to make corrections to the assignments, for partial credit, and still potentially receive up to four credits towards graduation.

This study had two main limitations. The first limitation was that during the study we were unable to determine if students were cheating during the interventions. While the second limitation was that there was a low probability of completing the coursework assigned during the intervention experiments.

3.2. Data Collection & Cleaning

All student records were gathered through separate spreadsheets from ESW and uploaded to Microsoft Excel for cleaning and testing. Once the data had been uploaded to excel, all student data was organized by ID number, to remove any potential duplicate entries. After all student data was organized, all variables except for last name, first name, gender, the subjects being investigated (Algebra 1, Transition to College Mathematics, Liberal Arts Mathematics I, and Geometry), and the completion date for each course were removed. All student data was then separated into their respective groups by usage of the intervention group rosters. All experimental groups were assigned based upon student need for graduation.

4. Results

To address research question 1, the interventions had the same impact on student performance. Experimental group 2 (n=108) is essentially double the size of experimental group 1 (n=47) and the average credits earned by each student in experimental group 2 () is essentially double the average credits earned per student in experimental group 1 (). Demonstrating that either intervention method resulted in students earning effectively half a credit more than if they did not participate in either intervention. Thus, failing to reject the null hypothesis for hypothesis 1.

The average number of credits earned during each intervention period were compared using a T-test, which can be seen in Table 2. For the average credits earned per student in all control groups are all relatively equal Demonstrating that the increased performance in the experimental groups are linked to the interventions that was provided. However, the results from the T-test failed to reject the null hypothesis for hypothesis 2, stating that the improved performance was not statistically significant.

Table 2. T-Test: Paired Two Sample for Means

Table 2: This tables displays the results of the T-test performed on the average credits earned during each experimental group.

5. Conclusion

This study determined that the provided academic intervention was successful in increasing student performance from 0 credits on average to credits on average earned per student in a three-month period. However, the interventions only increased student progress by 7.69%, which is not a significant impact on student performance. These interventions are specific to the unique LE that was used in the experiment. These results cannot be generalized to a standard LE. These interventions are still far from reaching, or improving upon, the expected number of credits that each student can earn within any given three-month period. The expected number of credits that each student is to earn in any given three-month period is 6.5 credits. However, intervention 2 should still be implemented due to greater number of students that can potentially be positively impacted, over intervention 1.

6. Future Research

Any future research in this unique LE should consist of determining what factors could be influencing the low levels of student performance other than learning environment/method. Since this study demonstrated that providing a traditional learning environment/method to non-digital learners in a digital LE was not a significantly successful method for improving student performance; providing evidence that the student performance issues are not solely linked to the LE. Other influencing factors for student performance could be found inside and outside of the SAI. Other factors inside of the SAI, besides the LE, could consist of the student’s personal motivations to come to the SAI or to complete their assigned work. However, during the 2021-2022 academic year a survey was conducted within the same SAI. The survey used a Likert-Scale to measure the average student’s motivation level to come to the SAI and to complete their assigned work. The Likert-Scale was from 1 to 5, 1 being not motivated at all and 5 being highly motivated. According to the survey, students are motivated to come to the SAI and to complete their assignments (= 3.5, 3.3). Demonstrating that student motivation levels to come to the SAI and to complete their assigned work are not influencing factors for student performance. This narrows the search for influencing factors for student performance to only factor that exist outside of the SAI.

References

[1]   | Belser, C. T., Prescod, D. J., Daire, A. P., Dagley, M. A., & Young, C. Y. (2017). Predicting Undergraduate Student Retention in STEM Majors Based on Career Development Factors. The Career Development Quarterly, 65(1), 88-93.
[2]   | Harris, R. B., Grunspan, D. Z., Pelch, M. A., Fernandes, G., Ramirez, G., & Freeman, S. (2019). Can Test Anxiety Interventions Alleviate a Gender Gap in an Undergraduate STEM Course? CBE—Life Sciences Education, 18(3), 35.
[3]   | Lang, C., Heffernan, N., Ostrow, K., & Wang, Y. (n.d.). The Impact of Incorporating Student Confidence Items into an Intelligent Tutor: A Randomized Controlled Trial. 6.
[4]   | McCabe, J. A., Kane-Gerard, S., & Friedman-Wheeler, D. G. (2020). Examining the utility of growth-mindset interventions in undergraduates: A longitudinal study of retention and academic success in a first-year cohort. Translational Issues in Psychological Science, 6(2), 132-146.
[5]   | Mills, I. M., & Mills, B. S. (2018). Insufficient evidence: Mindset intervention in developmental college math. Social Psychology of Education, 21(5), 1045-1059.
[6]   | Organization for Economic Cooperation and Development (OECD), Program for International Student Assessment (PISA), 2000, 2003, 2006, 2009, 2012, 2015, and 2018 Reading, Mathematics and Science Assessments.
[7]   | Oxford Dictionary. (Location: Oxford University Press, 2022), Oxford English Dictionary, 2nd ed.
[8]   | The Digital Learner. Pearson. (n.d.). Retrieved from https://www.pearson.com/us/higher-education/customers/institutional-leaders/digital-learner.html.
[9]   | Zemelman, S., Daniels, H., & Hyde, A. A. (2012). Best practice: Bringing standards to life in America's classrooms (4th ed.). Heinemann.