Implementation of Online Reading Assessments to Encourage Reading Interests
Bibliographic record
Abstract
The current study reports a two-year research project funded by the Government of the Republic of Indonesia through a competitive research scheme. The aim is basically to respond to the fact most university students have very low interests in reading activities, such as finding out important information for their term papers as assigned by the lectures. Instead, most of the time is spent in BBM, IPhone chats and Facebooking of non-academic nature (mostly social encounters). This has triggered a team of researchers to find out ways to increase or encourage reading interests. Internet browsing was undertaken to search for possible software application systems which could be used to administer online assessments in reading class. It was Question Writer (QW3.5) selected for use in the current study. It is a paid software application system especially developed for online assessments. It can perform various types of question formats with the students’ responses directly forwarded to the teacher’s email, and feedbacks and scorings automatically performed by the system. In the current study, a discussion group called ‘Reading Maniacs’ was created in Facebook for the students to get access to both reading materials and assessments. Questionnaire and interviews were conducted to investigate how the students got motivated in reading class and if their reading interests got increased. The findings indicate that the students were very motivated to participate in the online assessments supported by Facebook group discussion, thereby their reading interests leveled up. It was therefore recommended that online assessment of reading skills be conducted as additional activities to the well-supervised offline reading examination. Future researchers may want to administer Questionnaire to the reading teachers to get some feedbacks.
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 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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".