The Effects of Technology on Cognition and Academic Achievement

By: Ruchama Benhamou  |  November 12, 2023

By Ruchama Benhamou, Managing Editor 

Rapid augmentation of technology has developed powerful tools such as smartphones, computers, internet access, and social media that enhance people’s lives in their daily routines, but little is being said about technology’s effects on cognition. To that end, Wilmer, Sherman, and Chein (2017) have conducted a review of research that aimed to examine the relationship between mobile technology conditioning and cognitive functioning. The study claims that a prudent usage of technology may positively affect human cognition; however, when one forms a habitual behavior of using technology, it may have negative impacts – both in the short and long term – on a user’s emotions, intellect, memory, and attention span. Similarly, Subrahmanyam, Kraut, Greenfield, and Gross (2000) have already discussed the impact of computer usage on both children and adolescents’ development.

In order to explore the link between technology and human cognition, this paper will present five scholarly and peer-reviewed articles that will offer a broader perspective on this issue. Each of these articles will report their respective purpose, methods, procedures, and results. The first two articles will discuss the impact of technology on cognition and academic achievements. The third article will relate a study that examined cognitive failures due to excessive usage of the internet and smartphones. The fourth article will further examine the relationship between technology and emotional intelligence among university students. As for the fifth article, which is rather intriguing, it will relate how the mere presence of one’s smartphone may impact one’s available cognitive resource. This paper will then proceed to synthesize the contents of these articles before drawing evidence in the guise of a conclusion.

The purpose of the study in the first two articles was to explore the relationship between internet usage and academic performance, including reading, spelling, and mathematical achievements. Suphasawat, Hongsanguansri, Seree, and Rotjananirunkit (2016) collected data based on a sample of 297 participants from grades 4-6. They used several tests consisting of both intelligence and academic performance. With regard to intelligence, the study used the Colored Progressive Matrices (CPM) for participants aged 5-11 and the Standard Progressive Matrices (SPM) for participants aged 12 and above. As for academic performance, the researchers used the Wide Range Achievement Test, Thai Edition (WRAT-Thai).

In addition, concerning the aforementioned measuring instrument, the CPM consisted of three sets of 12 multiple-choice questions, thus comprising 36 items. Each participant completed the questionnaire using a computer program. The WRAT-Thai, which was used for participants aged 12 and above, consisted of three main categories: writing, reading, and math. The writing section included 50 questions; the reading section comprised 60 questions based on some reading passages participants read aloud. The math section included 44 math problems to be solved in writing. All testing was conducted under the supervision of professional psychologists.

Furthermore, the data analysis was two-fold: descriptive statistics and analytical statistics. The descriptive statistics related background information such as gender, age, school grade, and school attended. The initial sample size was 367 participants, but upon screening and filtering through all the criteria, only 297 participants remained. Participants were randomly selected via the Office of Elementary School in Bangkok. The second part of the descriptive statistics related the usage of internet and technology devices such as smartphones, tablets/iPads, computers/laptops, MP3s/iPods, and mobile games. In addition, the data related whether participants had rules versus no rules for usage.

Suphasawat et al. tested three hypotheses based on the descriptive statistics. The first hypothesis consisted of examining the relationship between the duration of internet usage and academic achievement; the second hypothesis focused on the motive of internet usage, and whether it influenced academic achievement; and the third hypothesis aimed to test the differences in academic achievement among students who received a tablet in 2016 (mainly grade 4), versus students who did not receive a tablet (mainly grades 5 and 6). Suphasawat et al. mainly used the Pearson’s Correlation to examine the relationship between internet usage and academic performance; and the t-test to understand whether there was a significant difference between the groups that received a tablet compared to the groups that did not.

Upon data analysis, Suphasawat et al. found that time spent on the internet and on technological devices were negatively correlated with all academic achievement sections (writing, reading, and math). However, results also indicated differences based on how the internet was used. For social-media use, the negative correlation was only significant with reading and math, but not writing. Results for entertainment usage revealed the same negative correlation but also included writing.. Surprisingly, internet usage for online shopping and selling – what the authors define as “business” – showed no correlation with academic achievements. Considering the results mentioned, Suphasawat et al. did find an overall negative correlation between internet usage and technology devices and academic achievement, except when internet usage was “business-oriented.”

In the same vein, Türel and Dokumaci (2022) conducted a study to explore the relationship between the use of media, academic procrastination, and academic achievement among adolescents. No fewer than 1,278 middle and high school students participated in the present study. Using the Convenience Sampling Method, the sample showed a certain homogeneity concerning the number of students per grade, as well as gender, which was very representative of the Turkish school system. The initial sample consisted of 1,630 participants. However, upon data screening, some recruitment questionnaires were missing values, or lacked a clear response; to that end, only 1,278 participants were retained for the purpose of this study.

The data collection included four sections: 1) demographic information, 2) media and technology usage, 3) academic procrastination, and 4) academic achievement, mainly measured by each participant’s grade point average (GPA). Demographic information consisted of 16 items related to school, gender, grade level, parents’ educational level, and ownership of technological devices such as smartphones, desktops, Twitter, Facebook, YouTube, Instagram, etc. In addition, the Media and Technology Usage (MTU) was scaled using the Rosen and Colleagues Scale (as cited in Türel and Dokumaci). The MTU generally comprises 15 subscales, however, Türel and Dokumaci only used 9 subscales, each including 40 items. Among those subscales were smartphone usage, general social media, internet searching, e-mailing, media sharing, text messages, video gaming, phone calls, and television watching.

With regard to academics, there were two sections mentioned above. Türel and Dokumaci used the Academic Procrastination Behavior Scale (ABP) developed by Cakici (as cited in Türel and Dokumaci). This instrument aimed to assess whether or not students could complete their academic tasks, such as studying, exam preparation, and attending classes. Participants’ responses were measured on a Likert Scale, 1 meaning “Does not reflect me at all” to 5 meaning “Reflects me completely.” As for the Academic Achievement score (AA), Türel and Dokumaci used participants’ GPAs. Based on other scholars’ suggestions, Türel and Dokumaci found that GPAs were a better scale to measure academic achievements. Türel and Dokumaci developed the following three hypotheses: 1) there is a positive association between MTU and APB, 2) there is a negative association between MTU and GPA, and 3) whether or not APB has a mediating effect on the association between MTU and GPA. In addition, Tamhane’s T2 Post-Hoc Test was conducted to examine interactions between middle and high school students. In order to test these hypotheses, Türel and Dokumaci drew descriptive statistics and conducted a t-test, ANOVA, and simple linear regression. Firstly, the findings revealed that MTU scores for female students were lower than for male students, suggesting that female participants were less inclined to use technology. Similarly, Tamhane’s T2 Post-Hoc Test showed that middle-school students used less technology than high-school students.

In the conclusion of this study, Türel and Dokumaci found a significant relationship between MTU and APB, thus supporting their first hypothesis. They further found a positive relationship between MTU and GPA, thus supporting their second hypothesis. However, similar to the previous study conducted by Subrahmanyam et al., MTU had a more negative effect on GPA when it was not academically oriented. As for the third hypothesis, Türel and Dokumaci also found that APB had a mediating role between MTU and GPA. One of the main effects concerned access to MTU and how easy it became to postpone all academic tasks versus spending time on social media, smartphones, and other devices.