Pith. sign in

REVIEW 4 major objections 5 minor 117 references

A Minimalistic Approach to Predict and Understand the Relation of App Usage with Students' Academic Performances

T0 review · 4 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read A 7-day snapshot of Android app usage, retrieved in under a second, predicts students' CGPA within ±0.36, and reveals that app-usage sessions, Productivity/Books use, and Video use relate to grades in distinct directions.

desk verdict Solid low-cost app-usage study with open code, but the headline correlations and prediction claims need statistical cleanup before they can be trusted. read the letter →

arxiv 2508.16779 v1 pith:DBZAB7VX submitted 2025-08-22 cs.HC

Md Sabbir Ahmed , Rahat Jahangir Rony , Mohammad Abdul Hadi , Ekram Hossain , Nova Ahmed This is my paper · ORCID
classification cs.HC
keywords appusageacademicperformanceCGPApredictionAndroidlogspassivesensingsessionscategoriesmachinelearning
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper argues that a minimal, unobtrusive data grab—seven days of real Android app-usage events pulled in about a third of a second—is enough to both characterize how phone behavior relates to grades and predict a student's cumulative grade point average. Analyzing 124 Bangladeshi undergraduates, it finds that the number of app-usage sessions, especially short micro and review sessions, carries a significant negative association with CGPA, while time in Productivity and Books & Reference apps is positively associated and time in Video apps is negatively associated. Social Media shows no significant association. If the paper is right, a single week of passively logged app activity can support early academic intervention without long-term sensing or self-reports.

What carries the argument

The load-bearing mechanism is the Android UsageStatsManager queryEvents API used to reconstruct raw foreground and background events for the past seven days without background sensing, combined with a session definition that joins app events separated by at most 45 seconds and splits sessions into micro (up to 15 seconds), review (15 to 60 seconds), and engage (over 60 seconds) types. Events are aggregated into 27 app categories and five time periods, producing 720 behavioral features; the authors select 44 features by significant correlation with CGPA and feed them into regression models, with KNN performing best. The key behavioral signal is session count: duration and launch frequency do not correlate with grades, but session counts do.

What would settle it

Collect the same seven-day app-usage snapshot and CGPA data from a new cohort in a different term, and check whether the session-count and Video correlations replicate; the central claim fails if a week without classes or during vacation removes the negative session association, or if a week-by-week longitudinal study shows that the seven-day features do not track semester GPA.

Watch

Extended reading notes

Core claim

The claimed discovery is that cumulative grade point average is readable from just the last seven days of app-usage events, and that the relationship is not one-dimensional. Aggregated measures such as total duration and launch count show no significant correlation with CGPA; instead, the total number of app-usage sessions, micro sessions, and review sessions does (Spearman coefficients around -0.20 to -0.24, p<0.05), with afternoon and evening sessions driving the effect. At the category level, Books & Reference and Productivity usage are significantly positively correlated with CGPA, while Video Players & Editors usage is significantly negatively correlated; Social Media is not. High and low CGPA holders differ in Productivity and Video behavior, and a K-nearest-neighbor model built from 44 correlation-selected features predicts CGPA with a mean absolute error of 0.36 on held-out data.

Load-bearing premise

The load-bearing premise is that one week of app usage, collected just before the Spring-2019 final exam, represents the long-term behavior that produced a cumulative CGPA; if that week is atypical, the correlations and predictions measure a transient state against a lifetime average.

Editorial extensions

If this is right

  • A student's phone can be read once, in under a second, and yield a grade forecast within ±0.36 of the actual CGPA, enabling early intervention before a semester's grades are final.
  • Interventions should target the number of app-usage sessions rather than total screen time; reducing micro and review sessions in the afternoon and evening may matter more than cutting overall duration.
  • Encouraging Productivity and Books & Reference use, and discouraging Video use—particularly morning video—should correlate with better outcomes if the associations reflect a modifiable pathway.
  • High and low CGPA students can be distinguished by category-level usage patterns, supporting personalized feedback and intervention designs.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Editorial inference: because CGPA is cumulative, a fairer test of the seven-day signal would be predicting semester GPA from the same week's logs; the ±0.36 error against CGPA may understate how tightly behavior tracks recent performance.
  • Editorial inference: the session-count result suggests a testable design—if students are nudged to consolidate phone use into fewer, longer sessions, and if the association is causal, grades should improve even without reducing total screen time.
  • Editorial inference: the morning-Video negative correlation implies that timing-aware interventions, such as deferring video watching to after class, could be more effective than blanket video blocks; this is an extension the paper does not test.
  • Editorial inference: the authors mention a second dataset collected during the COVID-19 pandemic; comparing category associations across pandemic and non-pandemic periods would test whether the Video and Books effects are stable or context-dependent.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. This paper presents an Android data-collection tool that retrieves the past seven days of app-usage events, and uses 124 Bangladeshi students' objective usage data and university-recorded CGPA to (i) correlate app-usage sessions and 27 app categories with CGPA, (ii) compare high/low CGPA holders and high/low users, and (iii) train regression models to predict CGPA. The headline claims are a significant negative association of session counts with CGPA, significant positive associations for Books and Productivity and negative for Video, no significant Social Media relation, and a KNN model whose test mean absolute error is 0.36 CGPA points on a held-out 30% of the data.

Significance. If the inferential claims survive re-analysis, the paper would be a useful contribution: objective 7-day app logs from a low-resource setting, direct category-level associations, and a lightweight predictor that does not require background sensing. Strengths include real usage events rather than self-report, actual CGPA from university records, open code and data-processing scripts, and a careful attempt to define sessions and diurnal patterns. However, two statistical issues—unreported or unverifiable multiple-comparison correction and outcome-based feature selection outside cross-validation—bear directly on the central claims and must be fixed before the results can be interpreted.

major comments (4)
  1. [§4.3.1, Tables 1–4] The manuscript states that p-values were adjusted using the false discovery rate approach, but every p-value shown in Tables 1–4 is a raw per-comparison p-value. For example, Table 1 has 9 comparisons; applying Benjamini–Hochberg to the smallest raw p=0.009 gives an adjusted p of about 0.081, which is not below 0.05. The session-level, Books, Productivity, and Video claims therefore cannot be evaluated as reported. Please state explicitly which analyses were FDR-adjusted, report the adjusted p-values or q-values, and show how many of the headline results survive correction.
  2. [§4.3.3, Table 9] The 44 features used by the final models were selected by testing correlation with CGPA on the full dataset of 124 participants before the train/test split. This uses test-set outcome information to choose features, so the reported test MAE of 0.36 and correlation of rs=0.44 are optimistically biased. Feature selection must be nested inside the cross-validation loop or performed only on the training portion of each fold before test evaluation. Without this, the abstract's 'predicts CGPA within 0.36' claim is not supported.
  3. [§4.1–§4.2] The outcome is cumulative CGPA from university records, while the predictors are seven days of usage collected just before the Spring-2019 final exam. A single pre-exam week may not represent the behavior that produced a multi-semester CGPA, for example because students cram, change phone-use patterns, or experience network or device problems during that week. The paper does not address this temporal mismatch or provide robustness checks, so both the correlational and predictive claims may be confounded by the timing of measurement. Please justify the proxy or report sensitivity analyses that address this concern.
  4. [Table 9] No baseline predictor is reported, so MAE=0.36 is hard to interpret. A model that always predicts the sample mean CGPA would have an MAE roughly equal to the mean absolute deviation of CGPA; reporting this baseline, along with R² or RMSE and the distribution of CGPA, would establish whether the app-usage features add predictive value beyond the marginal distribution.
minor comments (5)
  1. [Abstract and §5.5] 'Within ±0.36' should be explicitly described as a mean absolute error on a 37-participant test set, not as a bound that holds for every individual student.
  2. [Table 3] Several cells have very small sample sizes (e.g., Art & Design with N=3) and report p-values of exactly 1.0 or 0; consider suppressing categories below a pre-specified number of users or reporting exact coefficients with a clear caution.
  3. [Table 6 and §5.3.2] The Social Media rows report p=0.98 for four different usage metrics; please verify these values and report the actual test statistics and effect sizes, since identical p-values across different metrics are surprising and are likely an artifact of the test procedure.
  4. [§4.3.1] The Z-score outlier rule with a threshold of 3 is stated, but the paper does not report how many observations were removed in each analysis; this should be quantified for reproducibility.
  5. [Tables 4 and 6] The 'light grey' shading for 'close to significant' results is not defined; please state the explicit p-value range or criterion used for this visual category.

Circularity Check

1 steps flagged · score 5.0 of 10

The ±0.36 CGPA prediction is partially a fitted-input result because the 44 model features were selected by their significant correlation with the very CGPA values later predicted, while the paper's correlation findings are otherwise independent empirical observations.

  1. fitted input called prediction [Section 4.3.3, 'Prediction of academic performance'; Section 5.5, 'Predicting Students' Academic Performance Using App Usage Data']
    "Apart from these approaches, we also select features using another strategy: select only the features which show a significant correlation (p < 0.05) with academic performance. ... Among the 720 features, this approach selected 44 features as important ... The KNN algorithm-based model demonstrates the best performance where the MAE value is 0.36 (Table 9). This says the predicted CGPA is within ±0.36 of the ground truth CGPA."

    The predictor's input features are chosen by thresholding each feature's correlation with the same CGPA values that are later 'predicted.' Section 4.3.3 describes this correlation filter as a pre-modeling step, and Section 5.5 reports the resulting 44 features and the ±0.36 test MAE without stating that the filter was nested inside the 5-fold cross-validation or the 70/30 train/test split. Thus the test students' CGPA labels influenced which features entered the model, so the reported test MAE is not an independent out-of-sample estimate. The 'within ±0.36' claim is partly a consequence of fitting the feature selector to the outcome, rather than a pure derivation from app usage data alone.

full rationale

The paper's central correlational and comparative claims (Sections 5.1-5.4) are ordinary empirical analyses: CGPA is taken from university records and app usage from Android logs, and no definition makes one a function of the other. The category-level associations (e.g., Video negative, Productivity and Books positive) are observed correlations, not derived identities. The main circularity burden is localized to Section 5.5, where feature selection by significant correlation with CGPA is performed outside the reported cross-validation/split and then the resulting model's MAE is presented as a prediction. Self-citations [47,48] are used only for grouping conventions (top/bottom third; CGPA >=3.5 vs <3.0) and are accompanied by independent references [1,15,26], so they are not load-bearing. The ambiguity about FDR-adjusted versus raw p-values in Tables 1-4 is a statistical reporting concern, not a circularity concern. Overall, the derivation is largely self-contained; the score reflects the outcome-based feature selection in the prediction pipeline rather than the correlation results.

Assumptions & free parameters 6 free parameters · 5 assumptions · 0 invented entities

The central claims rest on several hand-set thresholds and domain assumptions. None are fit to the target outcome except the ML feature-selection and hyperparameters, which are part of the predictive pipeline rather than new entities.

free parameters (6)
  • Session gap threshold = 45 seconds
    Used to group app events into sessions (Section 4.2); value taken from Berkel et al. but not fitted to these data, and the central negative session-CGPA correlation depends on this choice.
  • Micro/review/engage session duration cutoffs = 15 s / 60 s
    Section 4.2 defines micro (<=15 s), review (15-60 s), engage (>60 s) sessions; these hand-chosen thresholds determine the session-type statistics in Tables 1, 2, and 5.
  • Diurnal period boundaries = Night 12-6, Morning 6-12, Afternoon 12-6, Evening 6-12
    Section 4.2 divides the day into four fixed periods; the period-specific correlations in Tables 2 and 4 depend on these boundaries.
  • High/low CGPA cutoffs = 3.50 / 3.00
    Section 4.3.1 classifies high and low CGPA holders using thresholds from prior studies; group comparisons in Table 8 depend on this split.
  • Outlier Z-score threshold = 3
    Section 4.3.1 removes outliers before choosing Pearson versus Spearman; this data exclusion rule can change the reported coefficients.
  • ML feature selection significance threshold = p < 0.05
    Section 4.3.3 selects 44 of 720 features by significant correlation with CGPA; the final KNN MAE of 0.36 depends on this threshold and on whether selection was nested inside cross-validation.
assumptions (5)
  • domain assumption Android UsageStatsManager queryEvents accurately reports app foreground/background events over the past 7 days.
    Section 3.1 relies on the Java class for ground-truth usage data; the authors validated against manual counts and third-party apps on 20 phones, but the API remains the foundation.
  • domain assumption Cumulative CGPA from university records is a valid measure of academic performance.
    Section 4.1 uses CGPA as the outcome; the university grading system and records are taken as reliable.
  • domain assumption The 7-day collection window is representative of the behavior that shaped cumulative CGPA.
    Section 4.2 collects only the past 7 days while Section 4.1 defines the outcome as cumulative CGPA; no evidence is given that one week generalizes.
  • domain assumption App categories assigned to 884 apps are correct.
    Section 4.2 uses Google Play categories, other app stores, and majority voting among students; mislabeling would shift category-level correlations.
  • domain assumption Students under snowball sampling are representative of the target population.
    Section 4.1 recruited 124 students from 3 departments of one Dhaka university; generalization to Bangladeshi students is an assumption.

how reviews work

0 comments
Cite this review

Pith. "Pith review of A Minimalistic Approach to Predict and Understand the Relation of App Usage with Students' Academic Performances." pith.science (2026). https://pith.science/paper/DBZAB7VX

@misc{pith2026250816779,
  author       = {Pith},
  title        = {Pith review of: A Minimalistic Approach to Predict and Understand the Relation of App Usage with Students' Academic Performances},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/DBZAB7VX}},
  note         = {Machine review of arXiv:2508.16779}
}
read the original abstract

Due to usage of self-reported data which may contain biasness, the existing studies may not unveil the exact relation between academic grades and app categories such as Video. Additionally, the existing systems' requirement for data of prolonged period to predict grades may not facilitate early intervention to improve it. Thus, we presented an app that retrieves past 7 days' actual app usage data within a second (Mean=0.31s, SD=1.1s). Our analysis on 124 Bangladeshi students' real-time data demonstrates app usage sessions have a significant (p<0.05) negative association with CGPA. However, the Productivity and Books categories have a significant positive association whereas Video has a significant negative association. Moreover, the high and low CGPA holders have significantly different app usage behavior. Leveraging only the instantly accessed data, our machine learning model predicts CGPA within 0.36 of the actual CGPA. We discuss the design implications that can be potential for students to improve grades.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

117 extracted references · 45 canonical work pages

  1. [1]

    Yiran Wang and Gloria Mark. 2018. The Context of College Students' Facebook Use and Academic Performance: An Empirical Study. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Sy stems (CHI '18). DOI: 10.1145/3173574.3173992

    arXiv 2018

  2. [2]

    Laura Silver. 2019. In emerging economies, smartphone adoption has grown more quickly among younger generations. Retrieved on 21 December, 2019 from https://www.pewresearch.org/global/2019/02/05/in -emerging- economies-smartphone-adoption-has-grown-more-quickly-among-younger-generations/

    2019

  3. [3]

    Dragan Lambić. 2016. Correlation between Facebook use for educational purposes and academic performance of students. Computers in Human Behavior 61: 313-320. DOI: 10.1016/j.chb.2016.03.052

    work page doi:10.1016/j.chb.2016.03.052 2016

  4. [4]

    Yanjun Yu. 2016. Facebook Usage and African American Student’s Academic Performance: An Ongoing A Minimalistic Approach to Predict and Understand the Relation of App Usage 193:23 PACM on Human-Computer Interaction, Vol. 7, No. MHCI, Article 193, Publication date: September 2023. Investigation. IOSR Journal of Research & Method in Education (IOSR-JRME) 6, 5: 84-88

    2016

  5. [5]

    Daniel Darghan Felisoni and Alexandra Strommer Godoi. 2018. Cell phone usage and academic performance: An experiment. Computers & Education 117: 175-187. DOI: 10.1016/j.compedu.2017.10.006

    work page doi:10.1016/j.compedu.2017.10.006 2018

  6. [6]

    Masiath Mubassira and Amit Kumar Das. 2019. The Impact of University Students’ Smartphone Use and Academic Performance in Bangladesh: A Quantitative Study. In Proceedings of the 13th International Conference on Ubiquitous Information Management and Communication (IMCOM '19). Advances in Intelligent Systems and Computing, 935: 734-

    2019

  7. [7]

    Sha Zhao, Julian Ramos, Jianrong Tao, Ziwen Jiang, Shijian Li, Zhaohui Wu,Gang Pan, and Anind K. Dey. 2016. Discovering different kinds of smartphone users through their application usage behaviors. In Proceedings. of the 2016 ACM UbiComp’16, 498 - 509. DOI: 10.1145/2971648.2971696

    arXiv 2016

  8. [8]

    Sedigheh Moghavvemi, Noor Ismawati Binti Jaafar, Ainin Binti Sulaiman, and Nafisa Kasem. 2017. Facebook and YouTube addiction: The usage pattern of Malaysian students. In ICRIIS '17, IEEE. DOI: 10.1109/ICRIIS.2017.8002516

    work page arXiv 2017

Show all 117 references
  1. [9]

    Syarif Husin Lubis,, Saiful Ridzuan, Ismarul Yusda Ishak, Hidayatul Fathi Othman, Nihayah Mohammed, Zariyantey Abdul Hamid, Nor Najwatul Akmal, Norazizah,Nurul Farahana, Shahida, Shanti, Nor Zafirah, Leong Li Hui, and Mohd Izham. 2012. The relationship between time spent on fa...

    2012 doi

  2. [10]

    Nebiat Negussie and Girum Ketema. 2014. Relationship between Facebook Practice and Academic Performance of University Students. Asian Journal of Humanities and Social Sciences (AJHSS) 2, 2

    2014

  3. [11]

    Inyeop Kim, Rihun Kim, Heepyung Kim, Duy eon Kim, Kyungsik Hana, Paul H Lee, Gloria Mark, and Uichin Lee

  4. [12]

    Tossell, Philip Kortum, Clay ton Shepard, Ahmad Rahmati, and Lin Zhong

    Chad C. Tossell, Philip Kortum, Clay ton Shepard, Ahmad Rahmati, and Lin Zhong. 2014. You can lead a horse to water but you cannot make him learn: Smartphone use in higher education. British Journal of Educational Technology 46, 4: 713-724. DOI: 10.1111/bjet.12176

    2014 doi

  5. [13]

    Ahmet Murat Uzun and S elcan Kilis. 2018. Does Persistent Involvement in Media and Technology Lead to Lower Academic Performance? Evaluating Media and Technology Use in Relation to Multitasking, Self -Regulation and Academic Performance. Computers in Human Behavior, 90: 196-20...

    2018 doi

  6. [14]

    Smartphone usage in the wild: A large -scale analysis of applications and context

    Trinh Minh Tri Do, Jan Blom, and Daniel Gatica -Perez. Smartphone usage in the wild: A large -scale analysis of applications and context. In Proceedings of the 13th International Conference on Multimodal Interfaces (ICMI’11), 353-

  7. [15]

    Campbell

    Rui Wang, Gabriella Harari, Peilin Hao, Xia Zhou, and Andrew T. Campbell. 2015. SmartGPA: How Smartphones Can Assess and Predict Academic Performance of College Students. In Proceedings of the 2015 ACM UbiComp ’15, 295-306. DOI: 10.1145/2750858.2804251

    2015

  8. [16]

    Retrieved on 23 rd August, 2020 from https://en.prothomalo.com/youth/education/86pc-university- students-own-smartphones-in-bangladesh-survey

    Mustak Ahmed. Retrieved on 23 rd August, 2020 from https://en.prothomalo.com/youth/education/86pc-university- students-own-smartphones-in-bangladesh-survey

    2020

  9. [17]

    Muhammad Faisal Masood, Aimal Khan, Farhan Hussain, Arslan Shaukat, Babar Zeb, and Rana Muhammad Kaleem Ullah. 2019. Towards the Selection of Best Machine Learning Model for Student Performance Analysis and Prediction. In 2019 ISCMI. DOI: 10.1109/iscmi47871.2019.9004299

    2019

  10. [18]

    Mehrak Rahim and Seyed Shahab Miri. 2014. The Impact of Mobile Dictionary Use on Language Learning. Procedia - Social and Behavioral Sciences 98: 1469 – 1474. DOI: 10.1016/j.sbspro.2014.03.567

    2014 doi

  11. [19]

    YouTube Needs: Understanding User’s Motivations to Watch Videos on Mobile Devices

    Rodrigo de Oliveira, Christopher Pentoney, and Mika Pritchard -Berman. YouTube Needs: Understanding User’s Motivations to Watch Videos on Mobile Devices. In Proceedings of the 20th MobileHCI '18 . DOI: 10.1145/3229434.3229448

  12. [20]

    Maya Samaha and Nazir S.Hawi. 2016. Relationships among smartphone addiction, stress, acad emic performance, and satisfaction with life. Computers in Human Behavior 57: 321-325. DOI: 10.1016/j.chb.2015.12.045

    2016 doi

  13. [21]

    Barkley, and Aryn C

    Andrew Lepp, Jacob E. Barkley, and Aryn C. Karpinski. 2014. The relationship between cell phone use, academic performance, anxiety, and Satisfaction with Life in college students. Computers in Human Behavior, 31: 343-350. DOI: 10.1016/j.chb.2013.10.049

    2014 doi

  14. [22]

    Iris Shuk Han Wai, Sara Sze Yan Ng, Dickson KW Chiu, Kevin KW Ho, and Patrick Lo. 2016. Exploring undergraduate students’ usage pattern of mobile apps for education. Journal of Librarianship and Information Science 50, 1: 34-47. DOI: 10.1177/0961000616662699

    2016 doi

  15. [23]

    Erdinç Aslan. 2016. A Study on the Use of Mobile Dictionaries in Vocabulary Teaching. Journal of Language and Linguistic Studies

    2016

  16. [24]

    Farah Nadia Azman, Syamsul Bahrin Zaibon, and Norshuhada Shiratuddin. 2016. A Study on User’s Perception towards Learner-generated Comics. International Review of Management and Marketing 6: 37-42

    2016

  17. [25]

    Adriana Teodorescu. 2015. Mobile learnin g and its impact on business English learning. Procedia - Social and Behavioral Sciences 180: 1535 – 1540. 193:24 Md Sabbir Ahmed et al. PACM on Human-Computer Interaction, Vol. 7, No. MHCI, Article 193, Publication date: September 2023

    2015

  18. [26]

    Yiran Wang, Melissa Niiya, Gloria Mark, Stephanie Reich, and Mark Warschauer. 2015. Coming of Age (Digitally): An Ecological View of Social Me dia Use among College Students. In Proceedings of the 18th ACM CSCW '15 571–

    2015

  19. [27]

    Reynol Junco. 2012. Too much face and not enough books: The relationship between multiple indices of Facebook use and academic performance. Computers in Human Behavior, 28: 187-198. DOI: 10.1016/j.chb.2011.08.026

    2012 doi

  20. [29]

    StatCounter. 2019. Mobile Operating System Market Share Bangladesh. Retrieved on December 26, 2019 from https://gs.statcounter.com/os-market-share/mobile/bangladesh

    2019

  21. [30]

    Baker, and Justin Daniel Cochran

    Jomon Aliyas Paul, Hope M. Baker, and Justin Daniel Cochran. 2012. Effect of online social networking on student academic performance. Computers in Human Behavior 28, 6: 2117-2127. DOI: 10.1016/j.chb.2012.06.016

    2012 doi

  22. [31]

    Vedran Sekara, Laura Alessandretti, Enys Mones, and Håkan Jonsson. 2021. Temporal and cultural limits of privacy in smartphone app usage. Scientific Reports 11. https://doi.org/10.1038/s41598-021-82294-1

    2021 doi

  23. [32]

    Campbell

    Rui Wang, Fanglin Chen, Zhenyu Chen, Tianxing Li, Gabriella Harari, Stefanie Tignor, Xia Zhou, Dror Ben -Zeev, and Andrew T. Campbell. 2014. StudentLife: Assessing Mental Health, Academic Performance and Behavioral Trends of College Students using Smartphones. In Proceedings o...

    2014

  24. [33]

    Valentin Kassarnig, Enys Mones, Andreas Bjerre -Nielsen, Piotr Sapiezynski, David Dreyer Lassen, and Sune Lehmann. 2018. Academic performance and behavioral patterns . EPJ Data Science 7, 1. DOI: 10.1140/epjds/s13688- 018-0138-8

    2018 doi

  25. [34]

    Camilla Turner. 2018. YouTube is bigger distraction to children’s homework than television, survey finds. Retrieved on 12th December 2019 from https://www.telegraph.co.uk/education/2018/02/23/youtu be-bigger-distraction- childrens-homework-television-survey/

    2018

  26. [35]

    Karpinski, and Paul A

    Ipek Ozer, Aryn C. Karpinski, and Paul A. Kirschner. 2014. A cross -cultural qualitative examination of social - networking sites and academic performance. Procedia - Social and Behavioral Scie nces 112: 873 -881. DOI: 10.1016/j.sbspro.2014.01.1244

    2014 doi

  27. [36]

    J. Clement. 2019. Most popular Google Play app categories as of 3rd quarter 2019, by share of available apps. Retrieved on December 18, 2019 from https://www.statista.com/statistics/279286/google -play-android-app- categories/

    2019

  28. [37]

    AppBrain. 2021. Most popular Google Play categories. Retrieved online on 25th December, 2021 from https://www.appbrain.com/stats/android-market-app-categories

    2021

  29. [38]

    Ofcom. 2018. A decade of digital dependency. Retrieved December 19, 2019 from https://www.ofcom.org.uk/about- ofcom/latest/features-and-news/decade-of-digital-dependency

    2018

  30. [39]

    Ibrahim Hossain Ovi. 2017. Dhaka is second most active city in world for Facebook users. Retrieved on January 14, 2020 from https://www.dhakatribune.com/bangladesh/dhaka/2017/04/14/dhaka-ranks-second-world-active- facebook-users/

    2017

  31. [40]

    Uichin Lee, Joonwon Lee, Minsam Ko, Changhun Lee, Yuhwan Kim, Subin YangKoji Yatani, Gahgene Gweon, Kyong-Mee Chung, and Junehwa Song. 2014. Hooke d on Smartphones: An Exploratory Study on Smartphone Overuse among College Students. In Proceedings of the SIGCHI Conference on Hu...

    2014

  32. [41]

    Nadia Rahmah, and Imas Sukaesih Sitan ggang. 2012. Determination of Optimal Epsilon (Eps) Value on DBSCAN Algorithm to Clustering Data on Peatland Hotspots in Sumatra. IOP Conf. Series: Earth and Environmental Science. DOI:10.1088/1755-1315/31/1/012012

    2012 doi

  33. [42]

    Yoav Benjamini and Yosef Hochberg. 1995. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society: Series B (Methodological) 57, 1: 289 –300. DOI: 10.1111/j.2517-6161.1995.tb02031.x

    1995

  34. [43]

    Leo Leppänen, Juho Leinonen, Petri Ihantola, and Arto Hellas. 2017. Predicting Academic Success Based on Learning Material Usage. In Proceedings of the 18th Annual Conference on Information Technology Education . DOI: 10.1145/3125659.3125695

    2017

  35. [44]

    Stamatis Karlos, Georgios Kostopoulos, and Sotiris Kotsiantis. 2020. Predicting and Interpreting Students’ Grades in Distance Higher Education through a Semi -Regression Method. Applied Sciences 10, 23: 8413. DOI: 10.3390/app10238413

    2020 doi

  36. [45]

    Muhammad Fazrul Ilahi Mohd Redzuan, Mohamad Amri Roslan, and Rosshairy Abd Rahman. 2015. Smartphone usage among ROTU and its relationship towards study performance. In AIP Conference Proceedings 1691, 1. DOI: 10.1063/1.4937103

    2015 doi

  37. [46]

    Snowball sampling

    Wikipedia contributors. Snowball sampling. In Wikipedia, The Free Encyclopedia. Retrieved on March 28, 2021, from https://en.wikipedia.org/w/index.php?title=Snowball_sampling&oldid=1013776327. A Minimalistic Approach to Predict and Understand the Relation of App Usage 193:25 P...

    2021

  38. [47]

    Sabbir Ahmed, Rahat Jahangir Rony, Md

    Md. Sabbir Ahmed, Rahat Jahangir Rony, Md. Mahir Ashhab, and Nova Ahmed. 2020. An Empirical Study to Analyze the Impact of Instagram on St udents’ Academic Results. In Proceedings of the 2020 IEEE Region 10 Symposium (TENSYMP’ 20). DOI: 10.1109/TENSYMP50017.2020.9230945

    2020

  39. [48]

    Sabbir Ahmed, Rahat Jahangir Rony, and Nova Ahmed

    Md. Sabbir Ahmed, Rahat Jahangir Rony, and Nova Ahmed. 2021. Identifying High and Low Academic Result Holders Through S martphone Usage Data. In Proceedings of the 2021 Asian CHI Symposium . DOI: 10.1145/3429360.3468192

    2021

  40. [49]

    Shlomit Hadad, Hagit Meishar-Tal, and Ina Blau. 2020. The parents’ tale: Why parents resist the educational use of smartphones at schools? Computers & Education 157: 103984. DOI: 10.1016/j.compedu.2020.103984

    2020

  41. [50]

    Michael Grace-Martin, Geri Gay. 2001. Web Browsing, Mobile Computing and Academic Performance. Educational Technology & Society, 4, 33

    2001

  42. [51]

    App Usage - Manage/Track Usage

    Sam Lu. App Usage - Manage/Track Usage. Retrieved on 28th March, 2021 from https://play.google.com/store/apps/details?id=com.a0soft.gphone.uninstaller

    2021

  43. [52]

    YourHour - Phone Addiction Tracker & Controller

    Mindefy Labs. YourHour - Phone Addiction Tracker & Controller. Retrieved on 28th March, 2021 from https://play.google.com/store/apps/details?id=com.mindefy.phoneaddiction.mobilepe

    2021

  44. [53]

    UsageStatsManager

    Google Developer. UsageStatsManager. Retrieved on 28th March, 2021 from https://developer.android.com/reference/android/app/usage/UsageStatsManager.html#queryEvents(long,%20long)

    2021

  45. [54]

    Antti Oulasvirta, Tye Rattenbury, Lingyi Ma, and Eeva Raita. 2011. Habits make smartphone use more pervasive. Personal and Ubiquitous Computing 16, 105–114. https://doi.org/10.1007/s00779-011-0412-2

    2011 doi

  46. [55]

    Suleiman Obeidat, Adnan Bashir, Wisam Abu Jadayil. 2012. The Importance of Class Attendanc e and Cumulative GPA for Academic Success inIndustrial Engineering Classes. In Proceedings of International Conference on Industrial Engineering, World Academy of Science, Engineering an...

    2012

  47. [56]

    Xi Chen and Afsaneh Doryab. 2020. Optimizing the Feature Selection Process for Better Accuracy in Datasets with a Large Number of Features (Student Abstract). In Proceedings of the AAAI Conference on Artificial Intelligence 34, 10: 13767–13768. DOI: 10.1609/aaai.v34i10.7155

    2020 doi

  48. [57]

    S choenebeck, and Julie A

    Alexis Hiniker, Sarita Y. S choenebeck, and Julie A. Kientz. 2016. Not at the Dinner Table: Parents’ and Children’s Perspectives on Family Technology Rules. In Proceedings of the 19th ACM CSCW’16. DOI: 10.1145/2818048.2819940

    2016

  49. [58]

    Jaejeung Kim, Hayoung Jung, Minsam Ko, and Uichin Lee. 2019. GoalKeeper: Exploring Interaction Lockout Mechanisms for Regulating Smartphone Use. In Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT) 3, 1: 1–29. DOI: 10.1145/3314403

    2019 doi

  50. [59]

    Kai Lukoff, Cissy Yu, Julie Kientz, and Alexis Hiniker. 2018. What Makes Smartphone Use Meaningful or Meaningless? Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2, 1 –26. https://doi.org/10.1145/3191754

    2018 doi

  51. [60]

    Yuxiang Lin, Wei Dong, and Yua n Chen. 2018. Calibrating Low -Cost Sensors by a Two-Phase Learning Approach for Urban Air Quality Measurement. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2, 1: 1–18. DOI: 10.1145/3191750

    2018 doi

  52. [61]

    I Just Want to Hack Myself to Not Get Distracted

    Ulrik Lyngs, Kai Lukoff, Petr Slovak, William Seymour, Helena Webb, Marina Jirotka, Jun Zhao, Max Van Kleek, and Nigel Shadbolt. 2020. “I Just Want to Hack Myself to Not Get Distracted.” In Proceedings of the 2020 CHI’20. DOI: 10.1145/3313831.3376672

    2020

  53. [62]

    Justin Cheng, Moira Burke, and Elena Goetz Davis. 2019. Understanding Perceptions of Problematic Facebook Use. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI’19) . DOI: 10.1145/3290605.3300429

    2019

  54. [63]

    Juan Pablo Carrascal and Karen Church. 2015. An In -Situ Study of Mobile App & Mobile Search Interactions. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (CHI’15) . DOI: 10.1145/2702123.2702486

    2015

  55. [64]

    Tapio Soikkeli, Juuso Karikoski, and Heikki Hammainen. 2011. Diversity and End User Context in Smartphone Usage Sessions. In Proceedings of 2011 Fifth International Conference on Next Generation Mobile Applications, Services and Technologies. DOI: 10.1109/ngmast.2011.1

    2011 doi

  56. [65]

    Matthias Böhmer, Brent Hecht, Johanne s Schöning, Antonio Krüger, and Gernot Bauer. 2011. Falling asleep with Angry Birds, Facebook and Kindle: a large scale study on mobile application usage. In Proceedings of the 13th MobileHCI ’11. DOI: 10.1145/2037373.2037383

    2011

  57. [66]

    Niels van Berkel, Chu Luo, Theodoros Anagnostopoulos, Denzil Ferreira, Jorge Goncalves, Simo Hosio, and Vassilis Kostakos. 2016. A Systematic Assessment of Smartphone Usage Gaps. In Proceedings of the 2016 CHI’16. DOI: 10.1145/2858036.2858348

    2016

  58. [68]

    Denzil Ferreira, Jorge Goncalves, Vassilis Kostakos, Louise Barkhuus, and Anind K. Dey. 2014. Contextual experience sampling of mobile application micro-usage. In Proceedings of the 16th MobileHCI ’14. DOI: 10.1145/2628363.2628367 193:26 Md Sabbir Ahmed et al. PACM on Human-Co...

    2014

  59. [69]

    Jaejeung Kim, Joonyoung Park, Hyunsoo Lee, Minsam Ko, and Uichin Lee. 2019. LocknType: Lockout Task Intervention for Discouraging Smartphone App Use. In Proceedings of the CHI’19. DOI: 10.1145/3290605.3300927

    2019

  60. [70]

    Dey, Grace Kao, and Carissa L ow

    Afsaneh Doryab, Anind K. Dey, Grace Kao, and Carissa L ow. 2019. Modeling Biobehavioral Rhythms with Passive Sensing in the Wild. In Proceedings of the ACM IMWUT 3, 1: 1–21. DOI: 10.1145/3314395

    2019 doi

  61. [71]

    Minsam Ko, Subin Yang, Joonwon Lee, Christian Heizmann, Jinyoung Jeong, Uichin Lee, Daehee Shin, Koji Yatani, Junehwa Song, and Kyong -Mee Chung. 2015. NUGU: A Group -based Intervention App for Improving Self - Regulation of Limiting Smartphone Use. In Proceedings of the CSCW’15

    2015

  62. [72]

    Stijn Baert, Sunčica Vujić, Simon Amez, Matteo Claeskens, Thomas Daman, Arno Maeckelberghe, Eddy Omey, and Lieven De Marez. 2019. Smartphone Use and Academic Performance: Correlation or Causal Relationship? Kyklos 73, 1: 22–46. DOI: 10.1111/kykl.12214

    2019 doi

  63. [73]

    Xing Xu, Jianzhong Wang, Hao Peng, and Ruilin Wu. 2019. Prediction of academic perf ormance associated with internet usage behaviors using machine learning algorithms. Computers in Human Behavior 98: 166 –173. DOI: 10.1016/j.chb.2019.04.015

    2019 doi

  64. [74]

    Hajra Waheed, Saeed -Ul Hassan, Naif Radi Aljohani, Julie Hardman, Salem Alelyani, and Raheel Na waz. 2020. Predicting academic performance of students from VLE big data using deep learning models. Computers in Human Behavior 104: 106189. DOI: 10.1016/j.chb.2019.106189

    2020

  65. [75]

    Faria Sana, Tina Weston, and Nicholas J. Cepeda. 2013. Laptop multitasking hin ders classroom learning for both users and nearby peers. Computers & Education 62: 24–31. DOI: 10.1016/j.compedu.2012.10.003

    2013 doi

  66. [76]

    Minhyung Kim, Inyeop Kim, and Uichin Lee. 2021. Beneficial Neglect: Instant Message Notification Handling Behaviors and Academic Performance. In Proceedings of the ACM IMWUT 5, 1: 1–26

    2021

  67. [77]

    Andreas Bjerre-Nielsen, Asger Andersen, Kelton Minor, and David Dreyer Lassen. 2020. The Negative Effect of Smartphone Use on Academic Performance May Be Overestimated: Evidence From a 2 -Year Panel Study. Psychological Science 31, 11: 1351–1362

    2020

  68. [78]

    Gonsalves James, Abraham Reem, Hande Shyamala, Rai Sharada, Bairy K, and Komattil Ramnarayan. 2014. Cumulative grade point average as a tangible predictor of academic achievement: A retrospectiv e cross-sectional study. Journal of Education Technology in Health Sciences, 1, 2

    2014

  69. [79]

    Inyeop Kim, Hwarang Goh, Nematjon Narziev, Youngtae Noh, and Uichin Lee. 2020. Understanding User Contexts and Coping Strategies for Context-aware Phone Distraction Management System Design. In Proceedings of the ACM IMWUT 4, 4: 1–33. DOI: 10.1145/3432213

    2020 doi

  70. [80]

    Inyeop Kim, Gyuwon Jung, Hayoung Jung, Minsam Ko, and Uichin Lee. 2017. Let’s FOCUS: Mitigating Mobile Phone Use in College Classrooms. In Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT) 1, 3: 1–29

    2017

  71. [81]

    Huaxiu Yao, Defu Lian, Yi Cao, Yifan Wu, and Tao Zhou. 2019. Predicting Academic Performance for College Students: A Campus Behavior Perspective. ACM Transactions on Intelligent Systems and Technology 10, 3: 1–21. DOI: 10.1145/3299087

    2019 doi

  72. [82]

    Sanbonmatsu, David L

    David M. Sanbonmatsu, David L. Strayer, Nathan Medeiros-Ward, and Jason M. Watson. 2013. Who Multi-Tasks and Why? Multi-Tasking Ability, Perceived Multi-Tasking Ability, Impulsivity, and Sensation Seeking. PLoS ONE 8, 1: e54402. DOI: 10.1371/journal.pone.0054402

    2013 doi

  73. [83]

    Fabian Okeke, Michael Sobolev, Nicola Dell, and Deborah Estrin. 2018. Good vibrations : Can a Digital Nudge Reduce Digital Overload?. In Proceedings of the 20th International Conference on Human -Computer Interaction with Mobile Devices and Services (MobileHCI’18)

    2018

  74. [84]

    Minsam Ko, Seungwoo Choi, Koji Yatani, and Uichin Lee. 2016. Lock n’ LoL: Group-based Limiting Assistance App to Mitigate Smartphone Distractions in Group Activities. In Proceedings of the CHI’16. DOI: 10.1145/2858036.2858568

    2016

  75. [85]

    Gloria Mark, Shamsi Iqbal, and Mary Czerwinski. 2017. How blocking distractions affects workplace focus and productivity. In Proceedings of the 2017 ACM UbiComp’17. DOI: 10.1145/3123024.3124558

    2017

  76. [86]

    Eyal Ophir, Clifford Nass, and Anthony D. Wagner. 2009. Cognitive control in media multitaskers. Proceedings of the National Academy of Sciences 106, 37: 15583–15587. DOI: 10.1073/pnas.0903620106

    2009 doi

  77. [87]

    Siddharth Misra, Oghenekaro Osogba, and Mark Powers. 2020. Unsupervised outlier detection techniques for well logs and geophysical data. In Machine Learning for Subsurface Characterization. Elsevier, 1–37. DOI: 10.1016/b978- 0-12-817736-5.00001-6

    2020 doi

  78. [88]

    Huckins, Weichen Wang, Alex daSilva, Courtney Rogers, Eilis Murphy, Elin Hedlund, Paul Holtzheimer, Shayan Mirjafari, and Andrew Campbell

    Mikio Obuchi, Jeremy F. Huckins, Weichen Wang, Alex daSilva, Courtney Rogers, Eilis Murphy, Elin Hedlund, Paul Holtzheimer, Shayan Mirjafari, and Andrew Campbell. 2020. Predicting Brain Functional Connectivity Using Mobile Sensing. In Proceedings of the ACM IMWUT 4, 1: 1–22

    2020

  79. [89]

    Yi Cao, Jian Gao, Defu Lian, Zhihai Rong, Jiatu Shi, Qing Wang, Yifan Wu, Huaxiu Yao, and T ao Zhou. 2018. Orderliness predicts academic performance: behavioural analysis on campus lifestyle. Journal of The Royal Society Interface 15, 146: 20180210

    2018

  80. [90]

    Sarah Diefenbach and Kim Borrmann. 2019. The Smartphone as a Pacifier and its Consequences: Young adults' smartphone usage in moments of solitude and correlations to self-reflection. In Proceedings of the 2019 CHI’19 DOI: A Minimalistic Approach to Predict and Understand the R...

    2019

  81. [91]

    They Don’t Leave Us Alone Anywhere We Go: Gender and Digital Abuse in South Asia

    Nithya Sambasivan, Amna Batool, Nova Ahmed, Tara Matthews, Kurt Thomas, Laura Sanely Gaytán -Lugo, David Nemer, Elie Bursztein, Elizabeth Churchill, and Sunny Consolvo. 2019. “They Don’t Leave Us Alone Anywhere We Go: Gender and Digital Abuse in South Asia.” In Proceedings of ...

    2019

  82. [92]

    Nova Ahmed, Tamanna Urmi, and Mahbuba Tasmin. 2020. Challenges and Opportunities for Young Female Learners in STEM from the Perspective of Bangladesh. In 2020 IEEE TALE. DOI: 10.1109/tale48869.2020.9368378

    2020

  83. [93]

    Kwok Tai Chui, Ryan Wen Liu, Mingbo Zhao, and Patricia Ordonez De Pablos. 2020. Predicting Students’ Performance With School and Family Tutoring Using Generative Adversarial Network-Based Deep Support Vector Machine. IEEE Access 8: 86745–86752

    2020

  84. [94]

    Zhanna Sarsenbayeva, Gabriele Marini, Niels van Berkel, Chu Luo, Weiwei Jiang, Kangning Yang, Greg Wadley, Tilman Dingler, Vassilis Kostakos, and Jorge Goncalves. 2020. Does Smartphone Use Drive our Emotions or vice versa? A Causal Analysis. In Proceedings of the CHI’20

    2020

  85. [95]

    Aurélien Géron. 2017. Hands-On Machine Learning with Scikit-Learn and TensorFlow. O'Reilly Media, Inc., USA

    2017

  86. [96]

    Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake Vanderplas, Alexandre Pas sos, David Cournapeau, Matthieu Brucher, Matthieu Perrot, Édouard Duchesnay. ...

    2011

  87. [97]

    Minsam Ko, Seungwoo Choi, Subin Yang, Joonwon Lee, and Uichin Lee. 201 5. FamiLync: Facilitating Participatory Parental Mediation of Adolescents' Smartphone Use. Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing - UbiComp ’15

    2015

  88. [98]

    Patricia Tachinardi. 2019. Diurnality. Encyclopedia of Animal Cognition and Behavior , 1 –3. https://doi.org/10.1007/978-3-319-47829-6_916-1

    2019 doi

  89. [99]

    Salvador Rodriguez. 2021. YouTube is social media’s big winner during the pandemic. CNBC. Retrieved April 24, 2023 from https://www.cnbc.com/2021/04/07/youtube-is-social-medias-big-winner-during-the-pandemic.html

    2021

  90. [100]

    David Creswell, Michael J

    J. David Creswell, Michael J. Tumminia, Stephen Price, Yasaman Sefidgar, Sheldon Cohen, Yiyi Ren, Jennifer Brown, Anind K. Dey, Janine M. Dutcher, Daniella Villalba, Jennifer Mankoff, Xuhai Xu, Kasey Creswell, Afsaneh Doryab, Stephen Mattingly, Aaron Striegel, David Hachen, Go...

    2023

  91. [101]

    Jennifer Elias. 2015. Why wearables are out of reach for people who need them most. Forbes. Retrieved April 24, 2023 from https://www.forbes.com/sites/jenniferelias/2015/10/27/the -leftovers-part-i-the-cost-of- activity/?sh=a0e5ffc63c9b

    2015

  92. [102]

    Mustak Ahmed. 2020. 86pc university students own smartphones in Bangladesh: Survey. Prothom Alo English. Retrieved April 24, 2023 from https://en.prothomalo.com/youth/education/86pc-university-students-own- smartphones-in-bangladesh-survey

    2020

  93. [103]

    Fehmi Ben Abdesslem, Andrew Phillips, and Tristan Henderson. 2009. Less is more: Energy-efficient mobile sensing with senseless. In Proceedings of the 1st ACM workshop on Networking, systems, and applications for mobile handhelds

    2009

  94. [104]

    Ayomide Owoyemi, Joshua Owoyemi, Adeneka n Osiyemi, and Andy Boyd. 2020. Artificial intelligence for healthcare in Africa. Frontiers in digital health 2: 6. https://doi.org/10.3389/fdgth.2020.00006

    2020

  95. [105]

    John Rooksby, Alistair Morrison, and Dave Murray -Rust. 2019. Student perspectives on digita l phenotyping: The acceptability of using smartphone data to assess mental health. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems

    2019

  96. [106]

    Nguyen, Quoc-Viet Pham, Pubudu N

    Dinh C. Nguyen, Quoc-Viet Pham, Pubudu N. Pathirana, Ming Ding, Aruna Seneviratne, Zihuai Lin, Octavia Dobre, and Won-Joo Hwang. 2023. Federated Learning for smart healthcare: A survey. ACM computing surveys 55, 3: 1–37. https://doi.org/10.1145/3501296

    2023 doi

  97. [107]

    MySahana. 2012. The effects of academic pressure on South Asian children. Cente r for Asian American Studies. Retrieved April 25, 2023 from https://liberalarts.utexas.edu/aas/news/the -effects-of-academic-pressure-on-south- asian-children

    2012

  98. [108]

    Senior Correspondent. 2022. Schoolgirl who jumped to her death from a building was ‘under p ressure’ for failing exams. bdnews24.com. Retrieved April 25, 2023 from https://bdnews24.com/society/w6qgl86vn1

    2022

  99. [109]

    Coming of age in the time of coronavirus: young lives and families in low - and middle -income countries

    Gina Crivello. Coming of age in the time of coronavirus: young lives and families in low - and middle -income countries. Ox.ac.uk. Retriev ed April 25, 2023 from https://www.ox.ac.uk/news/science -blog/coming-age-time- coronavirus-young-lives-and-families-low-and-middle-income

    2023

  100. [110]

    Cristian Alonso, Siddharth Kothari, and Sidra Rehman. 2020. Imf.org. Retrieved April 25, 2023 from https://www.imf.org/en/Blogs/Articles/2020/12/02/blog-how-artificial-intelligence-could-widen-the-gap-between- rich-and-poor-nations

    2020

  101. [111]

    UsageStatsManager

    Google Developer. UsageStatsManager. Retrieved on 2 7th April, 202 3 from https://developer.android.com/reference/android/app/usage/UsageStatsManager#queryUsageStats(int,%20long,%20 193:28 Md Sabbir Ahmed et al. PACM on Human-Computer Interaction, Vol. 7, No. MHCI, Article 193...

    2023

  102. [112]

    Razmin Bari and Farhana Sultana. 2021. Second Wave of COVID-19 in Bangladesh: An Integrated and Coordinated Set of Actions Is Crucial to Tackle Current Upsurge of Cases and Deaths. Frontiers in Public Health 9. https://doi.org/10.3389/fpubh.2021.699918

    2021

  103. [113]

    Gonzalo Gabriel Méndez, Luis Galárraga, Katherine Chiluiza, and Patricio Mendoza. 2023. Impressions and Strategies of Academic Advisors When Using a Grade Prediction Tool During Term Planning. Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. https:...

    2023

  104. [114]

    Mon Majhi

    Md Sabbir Ahmed. Mon Majhi. Retrieved July 4, 2023 from https://play.google.com/store/apps/details/?id=net.mn.u

    2023

  105. [115]

    Oliphant, Matt Haberland, Tyler Reddy, and David Cournapeau, et al

    Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, and David Cournapeau, et al. 2020. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods 17, 261 –272. https://doi.org/10.1038/s41592-019-0686-2 Received: January 2023...

    2020 doi

  106. [360]

    DOI: 10.1145/2070481.2070550

  107. [582]

    DOI: 10.1145/2675133.2675271

  108. [748]

    DOI: 10.1007/978-3-030-19063-7_59

    doi

  109. [2019]

    Computers & Education

    Understanding Smartphone Usage in College Classrooms: A Long -term Measurement Study. Computers & Education. DOI: 10.1016/j.compedu.2019.103611

    2019

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.