Implementation of Online Reading Assessments to Encourage Reading Interests
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<p>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.</p>
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it