Między słowem a gestem: konsekwencje stosowania technologii wspomagających czytanie i pisanie

Autor

Abstrakt

I summarize findings from studies conducted over the last 20 years regarding writing and reading on digital devices. In this literature review, the aim is to explore the effects on human cognitive ability of such functions as predictive text suggestions, gesture writing, collaborative writing, and visual-syntactic text formatting (VSTF). I also consider how writing patterns can be used. Studies have shown that auto-suggestions can significantly change the final message and make it less original. The way in which the content is displayed has a huge impact on how it is perceived. VSTF promotes careful reading, improves memory, and facilitates text analysis in older students. Comparing handwriting to writing using digital devices has demonstrated the importance of visual aspects for the recognition and copying of letters. Handwritten notes improve memory and stimulate deeper levels of cognitive function. The use of VSTF and co-editing documents can be most beneficial to low-level language learners. A more in-depth analysis is needed of emotions’ impact on forms of collaboration as well as the efficiency and multimodality of text input on comprehension.

Bibliografia

• Abrams Z.I., Collaborative Writing and Text Quality in Google Docs, „Language Learning & Technology” 2019, t. 23, nr 2, s. 22–42.

• Arnold K.C., Chauncey K., Gajos K.Z., Predictive Text Encourages Predictable Writing, [w:] Proceedings of the 25th International Conference on Intelligent User Interfaces, New York 2020, s. 128–138.

• Arnold K.C., Gajos K.Z., Kalai A.T., On Suggesting Phrases vs. Predicting Words for Mobile Text Composition, [w:] Proceedings of the 29th Annual Symposium on User Interface Software and Technology, New York 2016, s. 603–608.

• Arnold N., Ducate L., Kost C., Collaborative Writing in Wikis. Insights from Culture Project in German Class, [w:] The Next Generation. Social Networking and Online Collaboration in Foreign Language Learning, red. L. Lomicka, G. Lord, San Marcos TX 2009, s. 115–144.

• Billah S. M., Ko Y.J., Ashok V., Bi X., Ramakrishnan I.V., Accessible Gesture Typing for Non-Visual Text Entry on Smartphones, [w:] Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, New York 2019, s. 1–12.

• Brooks L., Swain M., Languaging in Collaborative Writing. Creation and Response to Expertise, [w:] Multiple Perspectives on Interaction in SLA, red. A. Mackey, C. Polio, Routledge, New York 2009, s. 58–89.

• Cao B. et al., Deepmood: Modeling Mobile Phone Typing Dynamics for Mood Detection, [w:] KDD '17 : proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining : August 13–17, 2017, Halifax, NS, Canada, New York 2017, s. 747–755.

• Carneiro D. et al., Multimodal Behavioral Analysis for Non-Invasive Stress Detection, „Expert Systems with Applications” 2012, t. 39, nr 18, s. 13376–13389.

• Cui W. et al., BackSwipe. Back-of-device Word-Gesture Interaction on Smartphones, [w:] Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, New York 2021, s. 1–12.

• Dukaj J., Po piśmie, Kraków 2019.

• Dutton E., van der Linden, D., Lynn, R., The Negative Flynn Effect. A Systematic Literature review, „Intelligence” 2016, t. 59, 163–169.

• Eder M., Metody ścisłe w literaturoznawstwie i pułapki pozornego obiektywizmu – przykład stylometrii, „Teksty Drugie” 2014, nr 2, s. 90–105.

• Exposito M., Hernandez J., Picard R.W., Affective Keys. Towards Unobtrusive Stress Sensing of Smartphone Users, [w:] Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services Adjunct, New York 2018, s. 139–145.

• Ghosh S. et al., Does Emotion Influence the Use of Auto-Suggest during Smartphone Typing?, [w:] Proceedings of the 24th International Conference on Intelligent User Interfaces, New York 2019, s. 144–149.

• Ghosh S. et al., Emotion Detection from Touch Interactions during Text Entry on Smartphones, „International Journal of Human-Computer Studies” 2019, t. 130, s. 47–57.

• Ghosh S. et al., Exploring Smartphone Keyboard Interactions for Experience Sampling Method driven Probe Generation, [w:] 26th International Conference on Intelligent User Interfaces, New York 2021, s. 133–138.

• Kessler G., Bikowski S., Developing Collaborative Autonomous Learning Abilities in Computer Mediated Language Learning. Attention to Meaning Among Students in Wiki Space, „Computer Assisted Language Learning” 2010, t. 23, nr 1, s. 41–58.

• Kessler G., Student-Initiated Attention to form in Wiki-Based Collaborative Writing, „Language Learning and Technology” 2009, t. 13, nr 1, s. 79–95.

• Khan I.A. et al., Measuring Personality from Keyboard and Mouse Use, [w:] Proceedings of the 15th European Conference on Cognitive Ergonomics: the Ergonomics of Cool Interaction, New York 2008, s. 1–8.

• Khan I.A., Brinkman W.P., Hierons R.M., Moods and Programmers Performance, Proceedings of the 19th Annual Workshop of the Psychology of Programming Interest Group, PPIG, Joensuu 2007, s. 3–16.

• Khan M.S., Khan I.A., Shafi M., Keyboard and Mouse Interaction Based Mood Measurement Using Artificial Neural Networks, [w:] 2012 International Conference of Robotics and Artificial Intelligence, IEEE 2012, s. 130–134.

• Kołakowska A., Szwoch W., Szwoch M., A Review of Emotion Recognition Methods Based on Data Acquired via Smartphone Sensors, „Sensors” 2020, t. 20, nr 21, 6367, 43 s.

• Kotuła K., Ocena telekolaboracyjnych projektów pisarskich realizowanych na platformach synchronicznych, „Neofilolog” 2020, nr 54/1, s. 171–187.

• Kretzschmar F. et al., Subjective Impressions Do Not Mirror Online Reading Effort. Concurrent EEG-Eyetracking Evidence from the Reading of Books and Digital Media, „PloS one” 2013, t. 8, nr 2, e56178, s. 1–11.

• Lobin H., Marzenie Engelbarta. Czytanie i pisanie w świecie cyfrowym, przeł. Ł. Musiał, Warszawa 2017.

• Mackare K., Jansone A., Mackars R., Use of Artificial Intelligence and Machine Learning for Personalization Improvement in Developed E-Material Formatting Application, [w:] Intelligent Computing. SAI 2020. Advances in Intelligent Systems and Computing, red. K. Arai, S. Kapoor, R. Bhati, Cham 2020, s. 125–132.

• McIntosh R.D. et al., Developmental Mirror-Writing is Paralleled by Orientation Recognition Errors, „Laterality. Asymmetries of Body, Brain and Cognition” 2018, t. 23, nr 6, s. 664–683.

• Merchant G., Digikids. Cool Dudes and the New Writing, „E-learning and Digital Media” 2005, t. 2, nr 1, s. 50–60.

• Moretti F., Wykresy, mapy, drzewa. Abstrakcyjne modele na potrzeby historii literatury, przeł. T. Bilczewski, A. Kowalcze-Pawlik, 2016.

• Oskoz A., Elola I., Promoting Foreign Language Collaborative Writing Through The Use of Web 2.0 Tools And Tasks, [w:] Technology-Mediated TBLT: Researching Technology and Tasks, red. M. Gonzalez-Lloret, L. Ortega, Amsterdam 2014, s. 115–148, https://doi.org/10.1075/tblt.6.05osk.

• Ozaki S., Ueda I., The Effects of Digital Scaffolding on Adolescent English Reading in Japan: An Experimental Study on Visual-Syntactic Text Formatting, „JALT CALL Journal” 2020, t. 16, 3, s. 147–165.

• Park Y. et al., Scaffolding Learning of Language Structures with Visual Syntactic Text Formatting, „British Journal of Educational Technology”, t. 50, nr 4, s. 1896–1912.

• Pietschnig J., Gittler G., A Reversal of the Flynn Effect for Spatial Perception in German-Speaking Countries. Evidence From a Cross-Temporal IRT-Based Meta-Analysis (1977–2014), „Intelligence” 2015, t. 53, s. 145–153.

• Pietschnig J., Voracek M., One Century of Global IQ Gains. A Formal Meta-Analysis of the Flynn Effect (1909–2013), „Perspectives on Psychological Science” 2015, t. 10, nr 3, s. 282–306.

• Piskioulis O., Tzafilkou K., Economides A., Emotion Detection through Smartphone’s Accelerometer and Gyroscope Sensors, [w:] Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization, red. J. Masthoff et al., New York 2021, s. 130–137.

• Roy S., Roy U., Sinha D.D., The Probability of Predicting Personality Traits by the Way User Types on Touch Screen, „Innovations in Systems and Software Engineering” 2019, t. 15, nr 1, s. 27–34.

• Sarsenbayeva Z. et al., Measuring the Effects of Stress on Mobile Interaction, „Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies” 2019, t. 3, nr 1, s. 1–18.

• Seyll L., Wyckmans F., Content A., The Impact of Graphic Motor Programs and Detailed Visual Analysis on Letter-like Shape Recognition, „Cognition” 2020, t. 205, 104443.

• Shehadeh A., Effects and Student Perceptions of Collaborative Writing in L2, „Journal of Second Language Writing” 2011, t. 20, nr 4, s. 286–305.

• Sparks A., Global Reading Scores Are Rising, But Not for U.S. Students, 2017, [online] https://www.edweek.org/teaching-learning/global-reading-scores-are-rising-but-not-for-u-s-students/2017/12 [dostęp 6.09.2021].

• Stephens-Davidowitz S., Abecadło testów A/B, [w:] tenże, Wszyscy kłamią. Big data i wszystko, co Internet może nam powiedzieć o tym, kim naprawdę jesteśmy, przeł. M. Świerkocki, Kraków 2019, s. 256–268.

• Stillman J., Good News from Science. Humanity Might Not Be Getting Dumber After All, 2017, [online] https://www.inc.com/jessica-stillman/humanity-might-not-be-getting-dumber-after-all-new-study-suggests.html [dostęp 6.09.2021].

• Storch N., Collaborative writing, „Language Teaching” 2019, t. 52, nr 1, s. 40–59.

• Storch N., Patterns of Interaction in ESL Pair Work, „Language Learning” 2002, t. 52, nr 1, s. 119–158.

• Trojahn M. et al., Emotion Recognition through Keystroke Dynamics on Touchscreen Keyboards, [w:] International Conference on Enterprise Information Systems, 2013, (3), s. 31–37.

• Umejima K. et al., Paper Notebooks vs. Mobile Devices: Brain Activation Differences during Memory Retrieval, „Frontiers in Behavioral Neuroscience” 2021, t. 15, s. 1–11.

• Walker S. et al., Visual-syntactic Text Formatting. A New Method to Enhance Online Reading, „Reading Online” 2005, t. 8, nr 6, s. 1096–1232.

• Watanabe Y., Swain M., Effects of Proficiency Differences and Patterns of Pair Interaction on Second Language Learning. Collaborative Dialogue between Adult ESL Learners, „Language Teaching Research” 2007, t. 11, nr 2, s. 121–142.

• Zaśko-Zielińska M., Listy pożegnalne. W poszukiwaniu lingwistycznych wyznaczników autentyczności tekstu, Wrocław 2013.

• Zhai S., Kristensson P.O., Interlaced QWERTY: Accommodating Ease of Visual Search and Input Flexibility in Shape Writing, [w:] Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, New York 2008, s. 593–596.

• Zhai S., Kristensson P.O., The Word-gesture Keyboard. Reimagining Keyboard Interaction, „Communications of the ACM” 2012, t. 55, nr 9, s. 91–101.

• Zhou J., Rau P.L.P., Salvendy G., Older Adults’ Text Entry on Smartphones and Tablets. Investigating Effects of Display Size and Input Method on Acceptance and Performance, „International Journal of Human-Computer Interaction” 2014, t. 30, nr 9, s. 727–739.

• Zysberg L., The Reversal of the Flynn Effect and Its Reflection in the Educational Arena. Data Comparison and Possible Directions for Future Research and Action, „Roczniki Pedagogiczne” 2019, t. 11, nr 3, s. 147–157.

Pobrania

Opublikowane

2023-01-30

Numer

Dział

Artykuły