Bibliographic record
Abstract
Over the last several years, we have explored ways to deliver distance developmental education. One lynchpin in the success of any distance education effort is access. In this column, we'll discuss how access is improving for developmental students. Once the issue of student access is addressed, the remaining two columns will address specific strategies for teaching developmental math, reading, and writing online. Access for Students Definitions of Access Access to technology is often defined by what students don't have: what is called a divide. Today, although over 429 million people are online, this represents only 6% of the world's population, with 41% of those online living in the U.S. and Canada (Benton Foundation, 2002). The U.S. Department of Commerce (2002) reports a digital divide for individuals online exists by ethnicity (Whites, 61%; Asian Americans, 73%; African American, 37%; Hispanics, 40%); by income (over $75,000, 90%; less than $15,000, 27%), by ethnicity and income (less than $15,000 and White, 21%; Asian American, 45%; African American, 9%; Hispanic, 13%), by education level (college graduates, 65%; less than high school, 12%), and by age (18-49 years of age, 63%; 50 or older, 37%). With the disproportionate number of minority, lower income, first-generation, and older students in developmental education (National Center for Educational Statistics, 2000), they are less likely to have online access. Some institutions collect a computer users' fee to expand access. This provides hardware, software, printer paper, and personnel to provide computer and online access for all students. Characteristics of Access Access can also be defined by what is available: for example, assistive technology for those with cognitive or physical disabilities. PEAT (Planning and Execution Assistant and Training system), through a personal digital assistant (PDA), helps developmental students with brain injury, Attention Deficit Disorder, Alzheimer's, or cognitive disorders plan daily tasks, maintain a schedule, remember directions or personal information, and remember tasks at a specific time (Attention Control Systems, 2002). iCommunicator (Interactive Solutions, 2002) provides the deaf or hearing impaired real-time translation allowing for speech-to-text, speech-to-video sign language, speech-to-- computer-generated voice, and text-to-computer-generated voice or video sign language. Wynn Wizard (Freedom Scientific, 2002) or Kurzweil 1000 (Kurzweil Educational Systems, 2002) provides screen readers for the blind or visually impaired. Other technology can provide assistive technology for the physically handicapped through devices for voice activation, switch access (controlling computers by breath puffs or pressing pads), and speech recognition; keyboards with large keys, overlays, eye or one-hand controls; and eye, foot, joystick, trackball, and touchpad mouse alternatives. For the learning disabled, text-to-speech and handheld spelling checkers are available (Ability Hub, 2002). The potential for developmental students with cognitive or physical handicaps using this technology is unlimited. Access also is defined by the speed of Internet connections. Highspeed access is available at a reasonable cost through cable TV modems and DSL (digital subscriber line), allowing connection speeds up to 3000 kps (kilobytes per second). Third generation wireless networks emerging over the next few years will allow our students to connect to the Internet with similar speeds through their cell phones or PDAs (Redman, 2002) and significantly reduce the costs of access. Faster access allows developmental distance educators to move from static webpages (that simply deliver handouts and samples) to dynamic webpages (webpages actually created for students as they enter information into a form). Faster access allows videoconferencing through one-way audio and video delivered to the desktop via programs like Blackboard (Blackboard, 2002), WebCT (WebCT, 2002), HorizonLive (HorizonLive, 2002 or Tegrity (Tegrity, 2002). …
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.493 | 0.241 |
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".