E-communication & Text Speak: Supporting Students' Literacy Development
Bibliographic record
Abstract
Previous literature addresses the relationship between e-communication, text speak, and students’ academic performance. The goal of the current study was to further investigate to what extent current literacy instruction reflects newer forms of e-communication, including text speak. The researcher identified what teachers see as the main benefits and challenges associated with e-communication and text speak, and gains an understanding of how teachers are making use of e-communication and text speak to support their literacy instruction. To collect this data, the researcher completed an in-depth literature review and conducted semi-structured interviews with junior teachers who incorporate social media/technology in their classrooms. The qualitative data collected from these interviews were analyzed and the following themes emerged: 1) The benefits of text speak and e-communication for students include increased engagement and more motivated writers; 2) E-communication devices allow for instant and ongoing communication and feedback, which can create student accountability and responsibility; 3) Text speak creates a problem for student literacy in terms of students demonstrating poor punctuation, capitalization, spelling, and sentence structure in their writing; 4) The challenges of e-communication and text speak for teachers include students' distractions with technology and parents' concerns; and 5) Teachers make use of e-communication devices to support their students' literacy instruction in a variety of ways including: a) increased organization and structure in the classroom; b) improved reading skills; and c) development of vital writing skills. This research study is timely and important because of the growing use of technology in students’ lives and how e-communication and text speak can be addressed in the classroom.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".