Profiles in Retention Part 1: Design Characteristics of a Graduate Synchronous Online Program.
Bibliographic record
Abstract
This paper is a case study of a Master of Library and Information Studies degree program online option that has been unusually successful in retaining and graduating students. Design characteristics of this program that has maintained a retention rate of over 90 percent over five years are described and mapped to the literature on distance education published after its implementation. The paper reports that characteristics identified in the practical and theoretical literatures are the same as, or closely related to, the characteristics of the program described. Student transcripts were used to track retention and time-to-completion. Topics for continuing study are identified and described. This is the first of two papers on retention in distance learning programs. The present paper (Part 1) addresses program design and implementation; Part 2 focuses on student responses to the online program option as they relate to student learning styles and outcomes.Keywords: LIS education, online learning, online education, distance education, student retention, enrollment attrition, program administration, student support services, case studyIntroductionThis is the first of two papers, a descriptive case study and a survey, that investigate the design characteristics of the synchronous online MLIS program option at the University of Alabama School of Library and Information Studies (UA SLIS) and its unusual success in retaining students to completion of degree program. As shown below, it has been reported for more than a dozen years that retention rates for distance education programs are lower than for programs in which instruction is delivered face-to-face. The UA Master of Library and Information Studies (MLIS) online program option is a 36-hour cohort-based option, drawing students from across North America, that has maintained a student retention rate after one year of greater than 90 percent and an overall graduation rate of greater than 90 percent over its first five successive entering classes.This paper is structured as follows. After reviewing the relevant literature, we provide a general description of the design characteristics of the UA MLIS online degree program option. In the Methods section, we describe the rationale for the methods employed and the steps for collecting retention data from the transcripts of 216 students who enrolled between 2005 and 2009. We determined retention rates at the half-way point for completion (after one year) and at the graduation point (after 2 years) for the first five years of the program's existence. In the final sections, we present findings and compare characteristics of the UA MLIS program with characteristics that the research literature identifies as related to high retention rates. Finally, we provide a preview of Part 2 of the study and suggest questions that remain to be explored.Review of Existing ResearchRetention of students in online educational programs has been the subject of numerous reports since the first online classes were taught in the early 1990s. There appears to be no clear retention or attrition rate for all programs, and the statistics are confounded by the number and types of programs studied (Perry, Boman, Care, Edwards, & Park, 2008). It has been established, through studies at several colleges, that attrition rates have been 10 to 30 percent higher for courses delivered online than for those delivered face-to-face (Carr, 2000).Throughout the decade from 2000 to 2010, dropout rates for online classes and programs were reported to range between 20 and 70 percent in secondary, undergraduate, continuing professional, and graduate courses in a wide range of disciplines (Angelo, Williams, & Natvig, 2007; Carr, 2000; Long, 2009; Tyler-Smith 2006). It must be recognized, of course, that attrition occurs among both online and face-to-face student populations: the Canadian Association of Graduate Schools (2004), for example, reported attrition rates for graduate degree programs (masters and doctoral levels) to range between 19 and 46 percent regardless of how instruction was delivered. …
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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".