Control ( 20)
The training period was 4–24 weeks (mean = 11.49; S.D. = 6.88). One study by Lee et al. had two length periods and total hours because the study examined video game training of two types. The total training hours were 16–90 h (mean = 40.63; S.D. = 26.22), whereas the training intensity was 1.5–10.68 h/week (mean = 4.96; S.D. = 3.00). One study did not specify total training hours. Two studies did not specify the training intensity. The training periods and intensities are in Table 8 .
Periods and intensities of video gaming intervention.
Author | Year | Length (Week) | Total Hours | Average Intensity (h/Week) |
---|---|---|---|---|
Gleich et al. [ ] | 2017 | 8 | 49.5 | 6.2 |
Haier et al. [ ] | 2009 | 12 | 18 | 1.5 |
Kuhn et al. [ ] | 2014 | 8 | 46.88 | 5.86 |
Lorenz et al. [ ] | 2012 | 8 | 28 | 3.5 |
Lee et al. [ ] | 2015 | 8–11 * | 27 | n/a |
Martinez et al. [ ] | 2013 | 4 | 16 | 4 |
Roush [ ] | 2013 | 24 | ns | n/a |
West et al. [ ] | 2017 | 24 | 72 | 3 |
West et al. [ ] | 2018 | 8.4 | 90 | 10.68 |
The training length was converted into weeks (1 month = 4 weeks). ns, not specified; n/a, not available; * exact length is not available.
Of nine eligible studies, one study used resting-state MRI analysis, three studies (excluding that by Haier et al. [ 40 ]) used structural MRI analysis, and five studies used task-based MRI analysis. A study by Haier et al. used MRI analyses of two types [ 40 ]. A summary of MRI analyses is presented in Table 9 . The related resting-state, structural, and task-based MRI specifications are presented in Table 10 , Table 11 and Table 12 respectively.
MRI analysis details of eligible studies.
MRI Analysis | Author | Year | Contrast | Statistical Tool | Statistical Method | Value |
---|---|---|---|---|---|---|
Resting | Martinez et al. [ ] | 2013 | (post- > pre-training) > (post>pre-control) | MATLAB; SPM8 | TFCE uncorrected | <0.005 |
Structural | Haier et al. * [ ] | 2009 | (post>pre-training) > (post>pre-control) | MATLAB 7; SurfStat | FWE corrected | <0.005 |
Kuhn et al. [ ] | 2014 | (post>pre-training) > (post>pre-control) | VBM8; SPM8 | FWE corrected | <0.001 | |
West et al. [ ] | 2017 | (post>pre-training) > (post>pre-control) | Bpipe | Uncorrected | <0.0001 | |
West et al. [ ] | 2018 | (post>pre-training) > (post>pre-control) | Bpipe | Bonferroni corrected | <0.001 | |
Task | Gleich et al. [ ] | 2017 | (post>pre-training) > (post>pre-control) | SPM12 | Monte Carlo corrected | <0.05 |
Haier et al. * [ ] | 2009 | (post>pre-training) > (post>pre-control) | SPM7 | FDR corrected | <0.05 | |
Lee et al. [ ] | 2012 | (post>pre-training) > (post>pre-control) | FSL; FEAT | uncorrected | <0.01 | |
Lorenz et al. [ ] | 2015 | (post>pre-training) > (post>pre-control) | SPM8 | Monte Carlo corrected | <0.05 | |
Roush [ ] | 2013 | post>pre-training | MATLAB 7; SPM8 | uncorrected | =0.001 |
* Haier et al. conducted structural and task analyses. + Compared pre-training and post-training between groups without using contrast. TFCE, Threshold Free Cluster Enhancement; FEW, familywise error rate; FDR, false discovery rate.
Resting-State MRI specifications of eligible studies.
Author | Year | Resting State | Structural | ||||||
---|---|---|---|---|---|---|---|---|---|
Imaging | TR (s) | TE (ms) | Slice | Imaging | TR (s) | TE (ms) | Slice | ||
] | 2013 | gradient-echo planar image | 3 | 28.1 | 36 | T1-weighted | 0.92 | 4.2 | 158 |
Structural MRI specifications of eligible studies.
Author | Year | Imaging | TR (s) | TE (ms) |
---|---|---|---|---|
Kuhn et al. [ ] | 2014 | 3D T1 weighted MPRAGE | 2.5 | 4.77 |
West et al. [ ] | 2017 | 3D gradient echo MPRAGE | 2.3 | 2.91 |
West et al. [ ] | 2018 | 3D gradient echo MPRAGE | 2.3 | 2.91 |
Task-Based MRI specifications of eligible studies.
Author | Year | Task | BOLD | Structural | ||||||
---|---|---|---|---|---|---|---|---|---|---|
Imaging | TR (s) | TE (ms) | Slice | Imaging | TR (s) | TE (ms) | Slice | |||
Gleich et al. [ ] | 2017 | win–loss paradigm | T2 echo-planar image | 2 | 30 | 36 | T1-weighted | 2.5 | 4.77 | 176 |
Haier et al. [ ] | 2009 | Tetris | Functional echo planar | 2 | 29 | ns | 5-echo MPRAGE | 2.53 | 1.64; 3.5; 5.36; 7.22; 9.08 | ns |
Lee et al. [ ] | 2012 | game control | fast echo-planar image | 2 | 25 | ns | T1-weighted MPRAGE | 1.8 | 3.87 | 144 |
Lorenz et al. [ ] | 2015 | slot machine paradigm | T2 echo-planar image | 2 | 30 | 36 | T1-weighted MPRAGE | 2.5 | 4.77 | ns |
Roush [ ] | 2013 | digit symbol substitution | fast echo-planar image | 2 | 25 | 34 | diffusion weighted image | ns | ns | ns |
All analyses used 3 Tesla magnetic force; TR = repetition time; TE = echo time, ns = not specified.
This literature review evaluated the effect of noncognitive-based video game intervention on the cognitive function of healthy people. Comparison of studies is difficult because of the heterogeneities of participant ages, beneficial effects, and durations. Comparisons are limited to studies sharing factors.
Video gaming intervention affects all age categories except for the children category. The exception derives from a lack of intervention studies using children as participants. The underlying reason for this exception is that the brain is still developing until age 10–12 [ 52 , 53 ]. Among the eligible studies were a study investigating adolescents [ 40 ], six studies investigating young adults [ 41 , 42 , 43 , 47 , 49 , 51 ] and two studies investigating older adults [ 48 , 50 ].
Differences among study purposes underlie the differences in participant age categories. The study by Haier et al. was intended to study adolescents because the category shows the most potential brain changes. The human brain is more sensitive to synaptic reorganization during the adolescent period [ 54 ]. Generally, grey matter decreases whereas white matter increases during the adolescent period [ 55 , 56 ]. By contrast, the cortical surface of the brain increases despite reduction of grey matter [ 55 , 57 ]. Six studies were investigating young adults with the intention of studying brain changes after the brain reaches maturity. The human brain reaches maturity during the young adult period [ 58 ]. Two studies were investigating older adults with the intention of combating difficulties caused by aging. The human brain shrinks as age increases [ 56 , 59 ], which almost invariably leads to declining cognitive function [ 59 , 60 ].
Three beneficial outcomes were observed using MRI method: grey matter change [ 40 , 42 , 50 ], brain activity change [ 40 , 43 , 47 , 48 , 49 ], and functional connectivity change [ 41 ]. The affected brain area corresponds to how the respective games were played.
Four studies of 3D video gaming showed effects on the structure of hippocampus, dorsolateral prefrontal cortex (DLPFC), cerebellum [ 42 , 43 , 50 ], and DLPFC [ 43 ] and ventral striatum activity [ 49 ]. In this case, the hippocampus is used for memory [ 61 ] and scene recognition [ 62 ], whereas the DLPFC and cerebellum are used for working memory function for information manipulation and problem-solving processes [ 63 ]. The grey matter of the corresponding brain region has been shown to increase during training [ 20 , 64 ]. The increased grey matter of the hippocampus, DLPFC, and cerebellum are associated with better performance in reference and working memory [ 64 , 65 ].
The reduced activity of DLPFC found in the study by Gleich et al. corresponds to studies that showed reduced brain activity associated with brain training [ 66 , 67 , 68 , 69 ]. Decreased activity of the DLPFC after training is associated with efficiency in divergent thinking [ 70 ]. 3D video gaming also preserved reward systems by protecting the activity of the ventral striatum [ 71 ].
Two studies of puzzle gaming showed effects on the structure of the visual–spatial processing area, activity of the frontal area, and functional connectivity change. The increased grey matter of the visual–spatial area and decreased activity of the frontal area are similar to training-associated grey matter increase [ 20 , 64 ] and activity decrease [ 66 , 67 , 68 , 69 ]. In this case, visual–spatial processing and frontal area are used constantly for spatial prediction and problem-solving of Tetris. Functional connectivity of the multimodal integration and the higher-order executive system in the puzzle solving-based gaming of Professor Layton game corresponds to studies which demonstrated training-associated functional connectivity change [ 72 , 73 ]. Good functional connectivity implies better performance [ 73 ].
Strategy gaming affects the DLPFC activity, whereas rhythm gaming affects the activity of visuospatial working memory, emotional, and attention area. FPS gaming affects the structure of the hippocampus and amygdala. Decreased DLPFC activity is similar to training-associated activity decrease [ 66 , 67 , 68 , 69 ]. A study by Roush demonstrated increased activity of visuospatial working memory, emotion, and attention area, which might occur because of exercise and gaming in the Dance Revolution game. Results suggest that positive activations indicate altered functional areas by complex exercise [ 48 ]. The increased grey matter of the hippocampus and amygdala are similar to the training-associated grey matter increase [ 20 , 64 ]. The hippocampus is used for 3D navigation purposes in the FPS world [ 61 ], whereas the amygdala is used to stay alert during gaming [ 74 ].
Change of the brain structure and function was observed after 16 h of video gaming. The total durations of video gaming were 16–90 h. However, the gaming intensity must be noted because the gaming intensity varied: 1.5–10.68 h per week. The different intensities might affect the change of cognitive function. Cognitive intervention studies demonstrated intensity effects on the cortical thickness of the brain [ 75 , 76 ]. A similar effect might be observed in video gaming studies. More studies must be conducted to resolve how the intensity can be expected to affect cognitive function.
Almost all studies used inclusion criteria “little/no experience with video games.” The criterion was used to reduce the factor of gaming-related experience on the effects of video gaming. Some of the studies also used specific handedness and specific sex of participants to reduce the variation of brain effects. Expertise and sex are shown to affect brain activity and structure [ 77 , 78 , 79 , 80 ]. The exclusion criterion of “MRI contraindication” is used for participant safety for the MRI protocol, whereas exclusion criteria of “psychiatric/mental illness”, “neurological illness”, and “medical illness” are used to standardize the participants.
Some concern might be raised about the quality of methodology, assessed using Delphi criteria [ 45 ]. The quality was 3–9 (mean = 6.10; S.D. = 1.69). Low quality in most papers resulted from unspecified information corresponding to the criteria. Quality improvements for the studies must be performed related to the low quality of methodology. Allocation concealment, assessor blinding, care provider blinding, participant blinding, intention-to-treat analysis, and allocation method details must be improved in future studies.
Another concern is blinding and control. This type of study differs from medical studies in which patients can be blinded easily. In studies of these types, the participants were tasked to do either training as an active control group or to do nothing as a passive control group. The participants can expect something from the task. The expectation might affect the outcomes of the studies [ 81 , 82 , 83 ]. Additionally, the waiting-list control group might overestimate the outcome of training [ 84 ].
Considering the sample size, which was 20–75 (mean = 43.67; S.D. = 15.63), the studies must be upscaled to emphasize video gaming effects. There are four phases of clinical trials that start from the early stage and small-scale phase 1 to late stage and large-scale phase 3 and end in post-marketing observation phase 4. These four phases are used for drug clinical trials, according to the food and drug administration (FDA) [ 85 ]. Phase 1 has the purpose of revealing the safety of treatment with around 20–100 participants. Phase 2 has the purpose of elucidating the efficacy of the treatment with up to several hundred participants. Phase 3 has the purpose of revealing both efficacy and safety among 300–3000 participants. The final phase 4 has the purpose of finding unprecedented adverse effects of treatment after marketing. However, because medical studies and video gaming intervention studies differ in terms of experimental methods, slight modifications can be done for adaptation to video gaming studies.
Several unresolved issues persist in relation to video gaming intervention. First, no studies assessed chronic/long-term video gaming. The participants might lose their motivation to play the same game over a long time, which might affect the study outcomes [ 86 ]. Second, meta-analyses could not be done because the game genres are heterogeneous. To ensure homogeneity of the study, stricter criteria must be set. However, this step would engender a third limitation. Third, randomized controlled trial video gaming studies that use MRI analysis are few. More studies must be conducted to assess the effects of video gaming. Fourth, the eligible studies lacked cognitive tests to validate the cognitive change effects for training. Studies of video gaming intervention should also include a cognitive test to ascertain the relation between cognitive function and brain change.
The systematic review has several conclusions related to beneficial effects of noncognitive-based video games. First, noncognitive-based video gaming can be used in all age categories as a means to improve the brain. However, effects on children remain unclear. Second, noncognitive-based video gaming affects both structural and functional aspects of the brain. Third, video gaming effects were observed after a minimum of 16 h of training. Fourth, some methodology criteria must be improved for better methodological quality. In conclusion, acute video gaming of a minimum of 16 h is beneficial for brain function and structure. However, video gaming effects on the brain area vary depending on the video game type.
We would like to thank all our other colleagues in IDAC, Tohoku University for their support.
PRISMA Checklist of the literature review.
Section/Topic | # | Checklist Item | Reported on Page # |
---|---|---|---|
Title | 1 | Identify the report as a systematic review, meta-analysis, or both. | 1 |
Structured summary | 2 | Provide a structured summary including, as applicable: background; objectives; data sources; study eligibility criteria, participants, and interventions; study appraisal and synthesis methods; results; limitations; conclusions and implications of key findings; systematic review registration number. | 1 |
Rationale | 3 | Describe the rationale for the review in the context of what is already known. | 1, 2 |
Objectives | 4 | Provide an explicit statement of questions being addressed related to participants, interventions, comparisons, outcomes, and study design (PICOS). | 2 |
Protocol and registration | 5 | Indicate if a review protocol exists, if and where it is accessible (e.g., Web address), and if available, provide registration information including registration number. | 2 |
Eligibility criteria | 6 | Specify study characteristics (e.g., PICOS, length of follow-up) and report characteristics (e.g., years considered, language, publication status) used as criteria for eligibility, giving rationale. | 2 |
Information sources | 7 | Describe all information sources (e.g., databases with dates of coverage, contact with study authors to identify additional studies) in the search and date last searched. | 2 |
Search | 8 | Present full electronic search strategy for at least one database, including any limits used, such that it could be repeated. | 2 |
Study selection | 9 | State the process for selecting studies (i.e., screening, eligibility, included in systematic review, and if applicable, included in the meta-analysis). | 3 |
Data collection process | 10 | Describe method of data extraction from reports (e.g., piloted forms, independently, in duplicate) and any processes for obtaining and confirming data from investigators. | 3 |
Data items | 11 | List and define all variables for which data were sought (e.g., PICOS, funding sources) and any assumptions and simplifications made. | 3 |
Risk of bias in individual studies | 12 | Describe methods used for assessing risk of bias of individual studies (including specification of whether this was done at the study or outcome level), and how this information is to be used in any data synthesis. | 2 |
Summary measures | 13 | State the principal summary measures (e.g., risk ratio, difference in means). | - |
Synthesis of results | 14 | Describe the methods of handling data and combining results of studies, if done, including measures of consistency (e.g., I ) for each meta-analysis. | - |
Risk of bias across studies | 15 | Specify any assessment of risk of bias that might affect the cumulative evidence (e.g., publication bias, selective reporting within studies). | - |
Additional analyses | 16 | Describe methods of additional analyses (e.g., sensitivity or subgroup analyses, meta-regression), if done, indicating which were pre-specified. | - |
Study selection | 17 | Give numbers of studies screened, assessed for eligibility, and included in the review, with reasons for exclusions at each stage, ideally with a flow diagram. | 3,5 |
Study characteristics | 18 | For each study, present characteristics for which data were extracted (e.g., study size, PICOS, follow-up period) and provide the citations. | 5-11 |
Risk of bias within studies | 19 | Present data on risk of bias of each study, and if available, any outcome level assessment (see item 12). | 5,6 |
Results of individual studies | 20 | For all outcomes considered (benefits or harms), present, for each study: (a) simple summary data for each intervention group (b) effect estimates and confidence intervals, ideally with a forest plot. | 4 |
Synthesis of results | 21 | Present results of each meta-analysis done, including confidence intervals and measures of consistency. | - |
Risk of bias across studies | 22 | Present results of any assessment of risk of bias across studies (see Item 15). | - |
Additional analysis | 23 | Give results of additional analyses, if done (e.g., sensitivity or subgroup analyses, meta-regression [see Item 16]). | - |
Summary of evidence | 24 | Summarize the main findings including the strength of evidence for each main outcome; consider their relevance to key groups (e.g., healthcare providers, users, and policy makers). | 12,13 |
Limitations | 25 | Discuss limitations at study and outcome level (e.g., risk of bias), and at review-level (e.g., incomplete retrieval of identified research, reporting bias). | 13 |
Conclusions | 26 | Provide a general interpretation of the results in the context of other evidence, and implications for future research. | 14 |
Funding | 27 | Describe sources of funding for the systematic review and other support (e.g., supply of data); role of funders for the systematic review. | 14 |
For more information, visit: www.prisma-statement.org .
D.B.T., R.N., and R.K. designed the systematic review. D.B.T. and R.N. searched and selected the papers. D.B.T. and R.N. wrote the manuscript with R.K. All authors read and approved the final manuscript. D.B.T. and R.N. contributed equally to this work.
Study is supported by JSPS KAKENHI Grant Number 17H06046 (Grant-in-Aid for Scientific Research on Innovative Areas) and 16KT0002 (Grant-in-Aid for Scientific Research (B)).
None of the other authors has any conflict of interest to declare. Funding sources are not involved in the study design, collection, analysis, interpretation of data, or writing of the study report.
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RODICA, PAULINE DENISE V. AND TALANIA, HANES ANDREW Department of Science, Technology, Engineering and Mathematics, Mount Carmel School of Maria Aurora (MCSMA), Silvestre Street Brgy. 2, Maria Aurora, Aurora, zip code 3202, in the school year 2019-2020. “EFFECTS OF ONLINE GAMES IN ACADEMIC PERFORMANCE AMONG SENIOR HIGH SCHOOL (SHS) STUDENTSOF MOUNT CARMEL SCHOOL OF MARIA AURORA.” The study dealt with the Effects of Online Games in Academic Performance among Senior High School (SHS) Students of Mount Carmel School of Maria Aurora. This aimed to determine the effects of online games among SHS in MCSMA. There were one hundred fifty + one (151) respondents which are composed of males and females from Academic Tracks which are Accountancy, Business and Management strand (ABM), General Academic strand (GAS), Humanities and Social Sciences strand (HUMSS). The study was laid out in descriptive design where researcher formulated questionnaire through Likert Scale. By collecting answers received from the surveys given out to the respondents, each criteria was tallied and was divided to the total number of tallies of all criteria; then, the quotient was converted to a percentage by multiplying it to 100. The parameters used to evaluate the result were the effects of online games among SHS in MCSMA. The result of the study showed that online game have negative effect to academic performance of Senior High School students of MCSMA. Study revealed that online gaming has a huge impact among them regarding on their academic performance which lead them to poor or low grade and physical distress as well. Majority of the respondents are replied and favored that online games gave negative outcome to their study and health. They found out that the students cannot focus on their studies, they cannot do their home works as well as their projects and that they have low grades. Based on the general result, the researchers conclude that a number of students playing online games could have a negative effect in their academic performance. Furthermore, students, teachers, and parents must be aware of the effects of playing online games and should regulate the time playing such game because it could ruin every students focus on their study. Students should be disciplined when it comes to playing online games which they could still perform satisfactorily in their studies and it should not be given much priority over higher and more realistic priorities.
KnE Social Sciences
Dennis Dumrique
lyra honrado
Meor Miqdad
Jambura Economic Education Journal
Ismail Lahay
The objective of this research was to findout the effect of online gaming habits on student,s learning outcomes in class X of social sciences in economics subject at SMA Negeri 1 Tapa, Bone Bolango Regency, It employed a quantitative method with a sample of 66 students. At the sama time, the data analysis used in this research was a simple linear regression analysis assisted by IBM Statistics SPSS 26.0 program. The research findings signified that the variable of online gaming habits partially had a negatif and significant effect on students’ learning outcomes in class X of social Sciences at SMA Negeri 1 Tapa, the results of this research obtainet a coefficient of determination (R2) of 0.169, meaning that the effect of online gaming habbits variabel on students’ learning outcomes at SMA Negeri 1 Tapa was 16.9%. in contrast, the remaining 83.1% was affected by other variables that contribute to students’ learning outcomes at SMA Negeri 1 Tapa, which were not examinet in this research.
Anne Tepace
JANAIAH ORIOQUE
Rosemarie Sumalinog Gonzales
This descriptive-correlational research study was undertaken in order to determine the effect of kids’ usage to internet games to the academic performance of Grade V and VI pupils in Ramon Magsaysay Central Elementary School. Data collected were analyzed using mean and Pearson r Correlation. Results revealed that the extent of kids’ usage to internet games was average and the level of academic performance of kids’ usage to internet games was also average. Regarding the study’s level of significance, it was found out that there was no significant relationship between kids’ usage to internet games and academic performance. Keywords: kids’ usage to internet games and academic performance
Dr. Rose Wyatt
This study determined the effect of computer games on the academic performance of grade six pupils in selected catholic schools in Davao City during the school year 2012-2013. This research was initiated to identify the game type most suitable to our teaching environment and to identify game elements that students found interesting or useful within the different game types. This study looked into the following: (1) the profile of the pupil-respondents in terms of (1) Pupils’ Profile: (1.1) gender; (1.2) parental monitoring; (1.3) type of computer games; (1.4) number of hours spent in playing on the computer; (1.5) game systems used. (2) Behavioral Factors: (2.1) pupils’ attitudes towards computer games; (2.2) study habits; (2.3) teachers’ perceptions about the pupils’ behaviour. (2) the academic performance of the pupils in terms of the following subjects: (2.1) English, Science and Mathematics. (3) the significant relationship between the academic performance of the pupil-respondents and the hours spent in playing computer games? The descriptive research design was employed in the study with the questionnaire as the instrument. The study made use of purposive sampling on the pupil-respondents and parent-respondents and random sampling in the selection of the teacher- respondents. The respondents were selected from three catholic schools namely: Ateneo de Davao University presently located in Matina, Davao City, Our Lady of Fatima Academy situated at Fatima St., Davao City and Assumption College of Davao in Cabagiuo St.,Davao City. The respondents were twenty-seven (27) teachers, and 218 pupils along with their parents. The analytical design was used in this study including the testing of the null hypothesis were the central tendency to utilize in the descriptive part of the analysis of data. Pearson-Product Moment of Correlation was used to test the reliability of the research instrument. Analysis of Variance was used to measure the significant difference between the academic performance of the pupils and the hours spent in playing computer games. On Respondents’ Profile, out of 218 pupil-respondents, there were 112 boys or (51.4 percent) and 106 girls or (48.6 percent). There were more boys than girls-respondents. On the other hand, on parental monitoring, out of ten indicators, five were rated agree. The parents want to monitor and guide their children because they know the positive and negative effects of playing computer games. This further revealed that pupils who are supported very well by their parents perform well in their studies. However, four of the indicators were rated disagree by the parents. The pupils strongly agree that playing computer games is for fun because they enjoy them. Whereas at school they are required to study many subjects that are boring to them. Playing computer games is actually an escape from the rigid rules and regulations they must follow at school. On the types of computer games played by the pupil-respondents, children answered with a multiple response. The majority of the respondents played action/fighting games. Second, are the online gaming sites followed by adventure & RPG Games, puzzle games, simulation, social networking sites and the last is the card games. The average of hours that the pupil-respondents played is three to four (3-4) hours in a day. Pupils have multiple responses about the game systems they used in playing computer games. Most of the respondents used the PC game system which was followed in use by the portable , table/phone and console game system. Pupils rated agree on the indicators that interactive games improve their logical thinking and reasoning; help them to become more computer literate; and creative; keep from getting bored until their friends are available to play and make new friends as well as strengthening their relationships with old friends. The pupils think that playing computer games is a positive experience to them and not a negative experience like their teachers, parents and other role models seem to believe. On study habits, the majority of the pupils studied their lessons on an average of 0-1 hour a day. On teachers’ perception on pupils’ behavior, the teacher-respondents agreed that playing computer manifests better computer skills and knowledge of facts, exhibits motor skills and hand-eye coordination and gain other skills, enhances creativity and inculcates a taste for graphics, and design and technology. However, it also manifests aggressive behaviors such as gets in many fights, cruelty, bullying, or meanness to others, doesn’t seem to feel guilty after misbehaving; develops attention problems like daydreaming (getting lost in thought and staring blankly); exhibits a decline in school achievements(repeated low grade, poor school work). On academic performance by subject, the pupil-respondents generally received a fair (80-84) to good (85-89) rating. Thus, their rating means that playing computer games do not have significant effect on their academic performance. However, there is an impact on their behaviour based on various researches. It was noted that English and Mathematics have no significant difference in the academic performance of the pupil-respondents in relation to hours spent in playing computer games. The result showed that thirty-seven percent explains the variation of the pupils’ academic performance which is due to playing computer games while sixty-three percent went to other factors that affect their performance in school. However, Science subjects showed significance on their academic performance. Perhaps, this subject requires a higher level of thinking skills. Some factors can be considered why these pupils did not perform in the said subject due to pupils’ study habits, attitudes towards the subject, thinking skills, peer and media influences. From the results and conclusions, it is recommended that the policy maker and school administrators will intensify the integration of Information Technology in the existing curriculum, improve lesson plan making using the computer –aided instructions (CAI) and provide more trainings/ seminars/ workshops/ to teachers that will equip them with IT skills. Lastly, design a Homeroom Guidance (HG) Activity on the effect of playing of computer games in their life.
Psychology and Education: A Multidisciplinary Journal
Psychology and Education
In modern society, computers have become almost a non-negotiable part of every individual's life. Then this is bound to have both positive and negative consequences on people. Because of this many young children and individuals anywhere can become addicted to playing such games online and offline gaming. It became a huge distraction to the academic performance of the learners by being addicted to computer games. The main goal of this study was to determine the level of understanding on playing in computer games and academic performance of learners in Sultan Palao Ali Memorial Elementary School, SPAMES (127217), Tagoloan District, Division of Lanao del Norte. The study used descriptive-correlational research design. Descriptive research determined the profile of elementary learners of Sultan Palao Ali Elementary School located at Barangay Inagonan, Tagoloan Lanao, Del Norte and the level of understanding on playing in computer games and the academic performance of the respondents. Based on the results of the study, most of the learners at Sultan Palao Ali Elementary School, Tagoloan District, Division of Lanao del Norte, were age ranges from 11-13, females and in satisfactory level as to their academic performance. In the level of understanding of playing computer games of the respondents, among the indicators of the level of understanding, the indicator "Playing computer games can enhance the accuracy/speed of my hands), got the highest mean score which can also be interpreted in the agreed level, while the indicator "Playing computer games can increase my empathy and supports my mental well-being) garnered the lowest mean of 1.80, which can be interpreted in the disagreed level in which the respondents believed that playing computer games negatively impacted their health. Further, in correlation, the null hypothesis, which states that there is no significant relationship between academic performance and profile in terms of age, was not rejected, while sex was rejected. At the same time, the null hypothesis, which states that there is no significant relationship between academic performance and the level of understanding of playing computer games, was also rejected. Furthermore, in the regression analysis, the null hypothesis stating that "there is no variable/s best predict the academic performance" was rejected.
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IMAGES
COMMENTS
parents and students of the potentially harmful effects of playing video games. The article contains 8 tables with two sections each (for male and female participants), and 47 cited entries in the list references. Keywords: video games, adolescence, teenagers, academic performance, school performance, school grades, school attendance
its effects on academic performance. ... Clark, C., Judge, T. (2020). A National Literacy Trust research report on Video game . playing and literacy: a survey of young people aged 11 to 16.
2. Literature review on video games and Students' success. Mainstream media and the general public often have the view that playing video games is harmful and dangerous [8], [9], [10].The authors of this paper are not gamers, but we have recollection of our childhood and how common it was to warn the youth that reading comics would make one stupid [11], or that Rock and Roll music was evil [12].
However, research on entertainment video gaming's effects on academic learning is still not extensive enough and remains mostly qualitative. Future studies need to provide a quantitative approach to complete and confirm already-existing literature, particularly in the environmental and social sciences, physical education, and programming.
This study examined the association of internet use, and electronic game-play with academic performance respectively on weekdays and weekends in Australian children. It also assessed whether ...
While some argue that video games can enhance cognitive skills, others claim that excessive gaming can lead to poor academic performance and even addiction. This research aims to investigate the influence of online video gaming on the academic performance of students. The study will examine the relationship between online gaming and academic ...
The purpose of the present research is to determine whether playing video games impacts academic performance as determined by GPA. To accomplish this, 198 participants filled out a Gaming Habits Survey which was analyzed using a series of one-way ANOVAs. The study found that there was a significant effect of player status on GPA at the p < .01 level for the two conditions of player versus non ...
these possible negative effects of video game usage on their academic performance. This research can serve as a foundation for future research on the impact of video game playing and student performance. Keywords: Videogames, GPA, Study Habits, Time Management Skills, Student Performance INTRODUCTION The video game industry has flourished to ...
Results. Multilevel models allowed the relationship between videogame use and academic performance to vary across countries and schools to obtain the best estimate of the effect of video-gaming on academic achievement .Results are displayed in Figure 1.As can be seen in the figure, there is no evidence that academic performance in science, mathematics or reading ability, declined as a function ...
A statistically significant correlation between video game usage and grade point average is found and this research can serve as a foundation for future research on the impact of video game playing and student performance. Playing video games has become one of the largest leisure activities in the world. This study examines the effects video games have on college students, their grade point ...
Whether playing video games impacts academic performance as determined by GPA is determined by a Gaming Habits Survey which was analyzed using a series of one-way ANOVAs and found that participants who indicated that they did play video games had significantly lower GPAs than participants who did not play videoGames. The purpose of the present research is to determine whether playing video ...
Analyses of the association between screen-based activities and academic performance areas were performed by subgroups of age: children were between 4 and 11.9 years of age, and adolescents were between 12 and 18 years of age. In addition, random-effects meta-regression analyses were conducted to examine whether age (in years) was a factor in ...
In present study 58.0% of video games players were 12-14 year's old, , 74% were boys and 46% video. game players were stage six. There was no significant association between age and academic ...
A 1997 study suggests that "there is no clear causal relationship between video game playing and academic performance" (The Effect of Videogames on Student Achievement, 2011). In 2005, a research paper suggested that video games are changing education and that games are more than a simple form of entertainment.
The aim of this paper is to examine the relationship between the time that higher education students spend playing video games during exam periods and their average grades in one Eastern European country. Moreover, the authors wanted to explore the differences among students with regard to their age, gender, year of study, and employment status in relation to video game-playing habits. Four ...
Overall, there is much that can be learned about video games and academic performance; this study is just one example of research done on a small level. Modern Psychological Studies (2011) 17: 37-44 42 References Anand, V. (2007). A study of time management: The correlation between video game usage and academic performance markers.
Purpose: This study investigated the effects of online gaming on the academic performance of students of DEBESMSCAT-Cawayan Campus. Methods: A descriptive research design was employed, and a ...
often and how long their child is allowed to play video games. The effects of electronic games and other factors in the grade five pupils' academic performance at A. Quezon elementary school, DEPED, Manila was looked into by Dorol (2009). Dorol concluded that electronic games were related significantly to pupils' academic
The game genres examined were 3D adventure, first-person shooting (FPS), puzzle, rhythm dance, and strategy. The total training durations were 16-90 h. Results of this systematic review demonstrated that video gaming can be beneficial to the brain. However, the beneficial effects vary among video game types.
Academia.edu is a platform for academics to share research papers. ... A 1997 study suggests that "there is no clear causal relationship between video game playing and academic performance" (Emes, 1997, p. 413). ... 2.2 Student Engagement and Sociological Effects Research on the social effects of video games is also mixed (Allison, Wahlde ...
Academia.edu is a platform for academics to share research papers. ... A 1997 study suggests that "there is no clear causal relationship between video game playing and academic performance" It goes on to say that the research is "sparse and contradictory" (Emes, 1997, p. 413). ... Students Engagement and sociological effects Research on ...
Caspian Journal of Health Research (CJHR) Research Paper Association Between Playing Video Games, General Health and Academic Performance of Fasa Mid-schools Boy Students ... Whereas, the effect size calculation showed moderate effect of video game on sleep duration (Cohen's d = 0.514) and mild effect on academic 398 Babu, Ravindra P N and ...
Local Studies Online Games/Video Games Playing video games is often associated in our society with poor academic performance. This anecdotal idea is supported by some research. The effect that 22. interactive digital media has on the learning process is not completely negative.