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Record W1516674963 · doi:10.18438/b8sd01

College Students in an Experimental Study Took Longer to Achieve Comprehension when Instant Messaging while Reading

2010· article· en· W1516674963 on OpenAlexvenueno aff
Megan von Isenburg

Bibliographic record

VenueEvidence Based Library and Information Practice · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsnot available
Fundersnot available
KeywordsReading (process)InstantInstant messagingHuman multitaskingReading comprehensionTask (project management)PsychologyComprehensionMathematics educationComputer scienceMultimediaWorld Wide WebCognitive psychologyLinguistics

Abstract

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A Review of: Bowman, L. L., Levine, L. E., Waite, B. M., & Gendron, M. (2010). Can students really multitask? An experimental study of instant messaging while reading. Computers & Education, 54, 927-931. Objective – To examine the effects of multitasking while doing school work. The experiment specifically measured total time spent reading a simulated textbook passage and tested comprehension in students who received instant messages before reading, while reading, or not at all. Design – Experimental design in which one group of students read an online text while receiving and responding to instant messages. Comparison groups either received instant messages (IMs) prior to reading the text passage or did not receive any IMs during the task. Setting – General psychology department at Central Connecticut State University, United States. Subjects – Eighty-nine college students enrolled in general psychology courses. The participants included 43 women and 46 men and were between 17 and 46 years old. Most students were full time students (91%), most were European / White (74%) and in their first (46%) or second (33%) year of college. Participants’ academic majors represented all the schools in the university. Methods – Researchers created a simulated environment in which a passage from a psychology textbook was displayed on five consecutive screens. For the experimental group, an IM appeared on each of the five screens preceded by an alert sound. Messages were written to reflect the types of questions students might ask each other when they first meet, such as “What do you like to do in your spare time?” Subjects were randomized to three situations: receiving IMs before reading, receiving IMs during reading, or not receiving any IMs. Subjects were told that they would either receive IMs before reading, while reading, or not at all. Messages received during reading appeared one per screen after a specified time spent on each page (after 17, 15, 29, 20 and 26 seconds, respectively.) Students could take as long as necessary to read the passage and to respond to IMs. After reading the passage, students were given a multiple choice test with 25 questions to determine reading comprehension and retention. Students also completed a demographic questionnaire to measure their typical instant messaging behaviour, including the amount of time they spend each week instant messaging, how often IM software is on when their computer is on, and how often IM software is on when they are studying. Both of these activities took place on the same computers used for the reading experiment. Students were additionally asked to comment on the clarity of instructions, the representativeness of the task to their typical IM experiences, and the interest and similarity to normal coursework of the reading itself. These questions were asked on paper rather than on the computer. Software recorded the lengths of time each student spent in reading the passage, reading and responding to IMs, and answering the online questions. For those students who received IMs during reading, the time spent from receipt of each IM to each response was subtracted from the total reading time. Main Results – There were no differences in test performance between the three groups. Statistically significant differences were found in the amount of time that students took to complete the reading: students who instant messaged during reading took significantly longer to read the online text than those students who instant messaged before reading and those who did not IM, even when time spent receiving and responding to IMs was subtracted from the totals. Students who instant messaged before reading took the least amount of time in the exercise. Further statistical analysis revealed no significant differences in the time spent instant messaging between the two IM groups. Responses to the demographic questions indicate that students spend a mean 7.5 hours instant messaging per week, that 67% of students have IM software on “sometimes,” “often,” or “very often” while the computer is on and 62% of the time while studying. Analysis indicated that none of the IM use variables were correlated with test performance or reading time and that there were no significant differences between the experimental groups according to prior IM use. Responses from the 77 students who answered the questions about the experiment itself are also included, though not all of these students answered each question. Seventy students (99%) agreed or strongly agreed that instructions were clear. Seventy-one percent of the 52 students that received IMs agreed or strongly agreed that they were realistic, and 75% agreed or strongly agreed that they responded to IMs in a typical manner. Sixty-two students (82%) agreed or strongly agreed that the text was similar to those assigned for actual coursework, and 39 students (51%) agreed or strongly agreed that the passage was interesting. Students commented on the authenticity of the experiment in free text responses such as, “I responded how I would have to anyone,” and “they were questions that anyone I don’t know might ask.” Conclusion – This experimental study suggests that students who IM while reading will perform as well but take longer to complete the task than those who do not IM while reading or those students who IM before reading.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0260.005

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.024
GPT teacher head0.343
Teacher spread0.320 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2010
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