Empowering Pre-Service Teachers for the Digital Transformation of Education from New Normal to Next Normal
Bibliographic record
Abstract
The purposes of research were to comparison of digital technology used for the instructional management during the Covid-19 pandemic during the New Normal period and the endemic Covid-19 during Next Normal period adopted by pre-service teachers; and to develop the landscape of digital technology used for instructional management during the New Normal and the Next Normal periods adopted by pre-service teachers. The participants included 400 pre-service teachers. The research tool was an online questionnaire. The data were analyzed by mean and standard deviation. The results showed the comparison of digital technology for the instructional management in the New Normal and the Next Normal as follows. Regarding the New Normal, the finding were: (a) some websites and applications such as Google Meet, Line, YouTube, and Facebook Messenger were used to communication with students; (b) some websites and applications, namely Google classroom, MS Teams, Moodle, and Thaimooc were used to send and receive student’s works; (c) some learning management systems, namely Google classroom, MS Teams, Moodle, and Thaimooc were used; (d) some digital media libraries such as YouTube, Trueplookpanya, DLTV, Satellite Distance Education Foundation, SciMath Knowledge Library, and IPST were used; (e) some online measurement and evaluation tools such as Google Forms, Kahoot, Quizizz, and Microsoft Forms were used and (f) some teaching guidelines and tools, like Video assignment, General Activities, and Attendance be used later. Regarding the Next Normal, the finding were: (a) some websites and applications were used to communicate with students, including Line, Facebook, Messenger, Youtube and Google classroom; (b) some websites and applications like Line, Facebook Messenger, YouTube, and Google classroom were used to send and receive student’s assignments; (c) some learning management systems like Google classroom, MS Teams, Moodle, Edmodo, and Thaimooc were also used; (d) some digital media libraries like Google classroom, YouTube, Trueplookpanya, OBEC Content Center - NEDA, and DLTV (Satellite Distance Education Foundation) were also helpful; (e) some online measurement and evaluation tools like Google Forms, Kahoot, Quizizz, and Microsoft Forms were also used; and (f) some teaching guidelines and tools like Assignment, Announcements and Update, Student Support, and Grade Book will also be used later. The digital technology landscape consists of 6 parts: (1) the websites and applications used for communication with students; (2) the websites and applications provides for students to submit their assignments; (3) the learning management systems; (4) the digital media libraries; (5) the online measurement and evaluation tools; and (6) the teaching guidelines and tools to be used later.
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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 | 0.001 | 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 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".