Bibliographic record
Abstract
AS the viability of using distance learning (DL) to reach students increases for school districts, there is a need for sound methodology on how to teach using the medium. How can instructors transition from relating to a warm body to relating to the camera? Are there established certification standards for instructors to teach over the information highway? What skills do instructors need to effectively teach using remote learning technologies? This article grew from a formative evaluation of the Direct Technical Assistance Model for Assisting Classroom Teachers in the Use of Technology to Improve Teaching and project in eastern North Carolina conducted by SERVE, Inc. A goal of this project was to identify and improve the skills teachers need to teach over the North Carolina Information Highway (NCIH) and provide certification standards for teachers to teach via the medium of telecommunications technologies. Certifying Teachers to Teach over the Information Highway Funded through the Technology Literacy Challenge Fund Sub-Grant Program, the project called for teachers and administrators in eastern North Carolina to participate in a series of workshops to prepare them to teach on the NCIH. As a beginning step in establishing standards for the state of North Carolina, the evaluator was tasked with determining what other states were doing regarding certification of teachers by: * Making inquiries on a distance education listserv, the Distance Education Online Symposium(1); * Reviewing relevant Web sites, including the US Distance Learning Association at www.usdla.org and the Distance Education and Training Council at www.detc.org; and * Searching for related literature on the ERIC online database (http://ericir.syr.edu) and in several distance education journals, including The American Journal of Distance Education (www.cde.psu.edu/acsde/jour.html), Distance Education: An International Journal (www.usq.edu.au/dec/decjournal/demain.html), From Now On: The Educational Technology Journal (www.fno.org), Syllabus: New Directions in Educational Technology (www.syllabus.com), and T.H.E. Journal (www.thejournal.com). Web and journal searches, though extensive, yielded little useful information. Numerous leads, provided by the members of the Distance Education Online Symposium (DEOS), led to information about a variety of short courses and degree programs designed to prepare teachers to teach via DL technology. The following examples illustrate the range of training options available to teachers: * A five-day Distance Education Certification Program offered through the Center for Distance Learning Research at Texas A&M University (www.cdlr.tamu.edu). The program includes 40 hours of competency-based training conducted on the campus of Texas A&M. To receive the Distance Education Professional Certificate, participants must attend all training modules, complete a group presentation, and perform a short assignment or Competency Transfer Activity. * California State University-Hayward offers an online program in online teaching and learning (www.online.csuhayward.edu/) for teachers, trainers, administrators, and instructional designers. The Certificate in Online Teaching and Learning consists of four 4.5-unit courses including Introduction to Online Teaching and Learning, Models for Online Instruction, Technology Tools for Online Instruction, and Designing Curriculum for Online Instruction. This program is not a state-sponsored credential program for K-12 teachers. * Athabasca University in Alberta, Canada offers a Masters Degree in Distance Education (www.athabascau.ca/html/depts/mde/mde broch.htm). The program was designed to cover key competencies in distance education, including systems concepts, instructional systems design, individualized instruction, developing an instructional product, and planning and management of distance education programs. …
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.039 | 0.009 |
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".