Integrating Literature with Technology and Use of Digital Tools: Impact on Learning Outcomes
Bibliographic record
Abstract
The teaching of literature involves understanding of emotions and feelings of the characters, plot constructions, setting, themes and drawing picturesque images. The aesthetics of teaching literature though lies in reading and understanding the text in its natural way; however, with the advent of technology and compulsive transition to online teaching, integration of technology and digital tools with teaching of literature has become a necessity. This study aimed at examining how to incorporate technology and digital tools in literature classrooms, to ensure attainment of learning outcomes. Digital tools currently adopted to teach literary texts include visualizations, digital editions of classics, storytelling through videos, graphic novels, interactive hypertexts and distant reading of the texts. Visualization tools, for example, can explain word patterns and sentence structure in a story, build digital artifacts, create digital maps of a novel’s setting, and convert themes into images. This study utilized a questionnaire survey with two learner groups, control and experimental, identified through purposive sampling, and in-depth interviews with six instructors who taught literature courses in a leading Saudi university. The focus of this mixed method research study was to see whether technology had done justice with the literary texts and helped achieve the intended learning outcomes. The study found out that with the help of technology students learned literary texts from multiple dimensions; however, the primary concern while integrating technology with teaching of literature should be to help students achieve learning outcomes. The study reiterated that whatever the media or the means to teach literature, if the learning objectives are achieved, combining technology with literature teaching will rather be a paradigm shift.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".