Have downloaded and upacked several EPUB files from Connexions and re-uploaded to own server. Will need to edit, hyperlink, and format docs.

Also analyzed one text for readability using Flesch with varying results. Will post those soon.

Chen, M.-chu. (2001). What can a mouse cursor tell us more? (sic) Correlation of eye / mouse movements on web browsing. Proceedings of the ACM Conference on Human Factors in Computing Systems (pp. 281-282).

One of the first studies to show strong correlations eye gaze position and computer cursor position asked users to browser several web sites while movements were tracked using EyeLink and a customized web browser. The web pages in the experiment were locally hosted–pre-loaded 3 levels deep, and page regions were categorized for ease of reference. No specific browsing instructions were provided. Looking at dwel time of both gaze and cursor by region, “The average of 595 correlations is 0.58. More than 50% of these pages were associated with correlations larger than 0.8.” And “eye gaze was directed to the (meaningful) new region in the end of the mouse saccade in 70% of the cases” Finally “The distribution of distances revealed that gaze/cursor distance under 35 pixels explained over 40 percent of the entire distances, which is about 1 degree of visual degree.” (282)

The researchers begin by noting “It is reasonable to assume that the relationship between cursor and gaze position will vary under different settings.” (281) A deep reading task would like show less correspondence. However, less mouse movement may by itself indicate deep reading–this alone may be a study worth undertaking, the question being, “what, if any, user input activity correspond to or indicate deep reading?”

Atterer, R., Wnuk, M., & Schmidt, A. (2006). Knowing the userʼs every move: user activity tracking for website usability evaluation and implicit interaction. Proceedings of the 15th international conference on World Wide Web (p. 203–212). ACM. Retrieved January 18, 2011, from http://portal.acm.org/citation.cfm?id=1135777.1135811.

This paper explains how user interaction can be tracked using a proxy web server employing client- and server-side scripting to record mouse movement and screen displays. The platform independent application constructed by the researchers is described in sufficient details to design a similar system. This system allows for the inexpensive collection of information on user behavior on web sites and in applications. The kinds of information include abstract and concrete, large and small. Possible applications include user profiling, debugging, usage analysis, usability testing, or even self-adapting web sites. Each of these applications is explored in greater depth.

The applications for this particular system with regards to usability are most closely aligned with my own interests, as some research has suggested as much as an 84% correlation between eye gaze movement and mouse movement. However, additional reading is necessary to determine if this is restricted to information finding behaviors, or also deeper reading.

Rodden, K., View, M., Fu, X., Aula, A., & Spiro, I. (2008). Eye-Mouse Coordination Patterns on Web Search Results Pages. Most, 2997-3002.

This study analyzed patterns of coordination between users eye movements and mouse movements when scanning a web search results page. Javascript was used to capture mouse coordinates at 100ms intervals, then, presumably using AJAX, sent the results to a server. 32 participants with a wide range of ages and searching experience were tasked with 16 search objectives using Google. Results are best presented visually, but patters of active mouse usage followed the eye horizontally (rare) and vertically (common), and was held to temporarily mark a particular result (more than half). Further work is needed to confirm these findings, and for generating “reliable and valid metrics” (3000).

Though exciting, this study is limited to web search scanning behavior, not the deeper reading activities that I am more interested in. Especially telling may be the fact that “‘following the eye horizontally’ behaviour was generally quite rare”. It may, however, be useful in studies that seek to describe distracted user behavior on web sites.

Notes

History of DE: 3 generations: correspondence, recorded media, networked communication. Defining DE: Keegan: separation of student and instructor, provisions for two-way communication, possibility of meetings. Simonson, Schlosser, Hanson: two-way synchronous/asynchronous interaction. “Distance education” includes learning and teaching. Examined The American Journal of Distance Education (AJDE) and Distance Education (DE). 38% primary research, 30% conceptual/theory, 14% lessons learned, 4% lit reviews, 4% evaluations (4). Many articles were anecdotal (5), and few were generalizable (6). Regarding primary research, most included surveys (5). Few studies were returned to. Anglin and Morrions argue educational research requires a diversity of methods (6). Theory-building will help DE develop.

Comments

None of the findings of this article were a surprise. I would have appreciated more scrutiny of the primary research articles discovered. The call for theory-building at the end is fine, but theories must be based on evidence, and this suggests generalizable evidence is scant.

Davies, R., Howell, S., Petrie, J. 2010. A review of trends in distance education scholarship at research universities in North America, 1998-2007.International Review of Research in Open and Distance Learning, 11(3).

Notes

The summary impression is that most dist ed “research” is descriptive, and not very good. This study sampled all dissertations as snapshots in 3 different years (5yrs apart), using the descriptor “distance education”. A summary of their results found that research addressed primarily student issues & faculty issues predominately, but also methods testing, pedagogical issues & instructional design. Research designs included descriptive quantitative survey, case study, quasi experimental, correlational. Techniques: t-test, anova, correlations.

Comments

This study looked at all dissertations in 3 years at 5yr intervals, but would a random sampling be more useful? “A priori coding” was used–was it chosen for convenience? One of the questions, what do these groups think about it?, seemed superficial. Also, in this particular study there seems to be a presumption was that distance ed == online. Though in late 20c that may be assumable
, there are clear exceptions. Also, defining distance education is no longer as simple as it was, as the spectrum has widened, and possibilities increased. If this were more than a survey of research articles, this assumption would be of concern. Question: What is a “theory-building study”?

Lee, Y., Driscoll, M. P., & Nelson, D. W. (2007). Trends in research: A content analysis of major journals. In M. G. Moore (Ed.), Handbook of distance education (Second., pp. 31-41). Mahwah, NJ: Lawrence Earlbaum Associates.

Notes

Comments

Most cited

Cohen, A. (1987). Instructional Alignment: Searching for a Magic Bullet. Educational Researcher, 16:8, 16-20.

Cohen reviews and expands on investigations into the effect on learning outcomes of instructional alignment. Cohen explains the history of instructional alignment, going back to the 60s, and notes that though “teaching what we assess, or assessing what we teaching seems embarrassingly obvious”(19) the fact that precise instructional alignment results in better learning outcomes has often been ignored or disdained or misunderstood. Testing whether the alignment effect is as large as it looks (“approximately four times the norm”), Cohen reviews several new studies. The Koczor Study (1984) showed that instructional alignment vs misalignment provided “effect sizes … for the lower and average aptitude students were as high as 1.10 and 2.74 sigma”. The Tallarico Study (1984) showed that lower achievers average score exceeded the 85th percentile of a placebo group, equating to a 1.3 sigma effect. The Fahey study (1986) found that alignment effect increased as students moved from easy to difficult tasks; also, higher aptitude students performed better than lower aptitude students on misaligned items; finally, lower aptitude students performed higher on aligned items than did the higher aptitude students on the misaligned items, with an effect size of 1.2 sigma (“For low achievers, a little alignment goes a long way.”). The Elia Study (1986) reported, overall, an alignment effect of 0.91sigma, though in the “phrase condition” it reached 1.76 sigma.

Comments:
Instructional alignment appears to be absent from Bloom’s initial consideration in the 2 sigma problem. Here, Cohen shows it’s importance by reviewing contemporary research studies–especially for low achievers. That the research studies often showed disparate effects for different conditions and learners implies the complexity of the 2 sigma problem, and perhaps indicts Bloom’s willingness to generalize results.

Aleven, V, Koedinger, K. (2002). An effective metacognitive strategy: learning by doing and explaining with a computer-based Cognitive Tutor. Cognitive Science 26, 147-179.

Using a computer software called Cognitive Tutor for instruction and assessment of high school geometry, the researchers compared pre-test and post-test performance of two groups of students; the experimental group was required to provide an explanation for their answers–otherwise conditions were the same. Experiment 1 found that the explanation students spent more time on task, and improved more on their post-test scores than the control. Experiment 2 controlled for time on task, but the results still suggested that the explanation group performed better on the post-test, and “learned better to explain their steps” (162). The researchers investigated issues od deep learning, and found that the explanation group performed better on “harder-to-guess” items, and “more likely to reflect on the sufficiency of their knowledge, and may have achieved better transfer of skills. Researchers’ conclusion: by engaging in the metacognitive strategy of explanation “students acquired better-integrated visual and verbal declarative knowledge and acquired less shallow procedural knowledge”.

Comments:
first, it was amazing to discover the specificity with which these researchers considered their experiment and executed it. Their description outweighs most others I have read on similar subjects. I believe this comes from their backgrounds in cognitivism, as they seem to be seeking to pinpoint domains as well as models/structures in order to be more accurate in their experiment and results. This made me wonder about other empirical research which, at least in reporting, includes less description and specificity. Second, though the researchers’ discussion of their results made sense to me, I was not familiar enough with their statistical methods to be able to fully comprehend the numbers reported for each of the 2 experiments or relate them to a “sigma” effect. Finally, this article, which targets a metacognitive strategy used by learners, also testifies to the importance of instructional design, and what is essentially an advance in programmed instruction that provides dynamic feedback and resources to the students, suggesting that many of the variables Bloom cites are too entangled or intertwined to isolate and recombine. These researchers’ own reference to Bloom is of a “potential” effect conditioned by “highly effective” one-on-one tutoring (they reference another study which had lesser effects from tutoring).

Oestmann, E. & Oestmann, J. (2006). Significant difference in learning outcomes and online class size. Journal of Online Educators, 2(1), 1-8.

This study examines the outcomes of 5 large (20>) and small (<10) online masters level courses to determine if there are significant difference in interactivity and final grades. Contrary to some expectations they found that the average final grade in the large class size was 5% higher than the smaller class size. Also, the quality of discussion forum posts was judged to be greater–more substantial–in the larger class. The researchers interpret this as reflective of Vygotsky's socio-cultural learning theory "in which more opportunities for social interaction resulted in higher measures of learning outcomes"

Comments:
Though this is not directly tied to Bloom's 2 sigma problem, it is related to aim to achieve that 1-1 ideal. This research suggests that in the new online environment large groups matter. This makes sense to me, and reinforces a suspicion I had about the 2 sigma problem's relevance in the face of our changing culture and communication media practices. I have reviewed other investigations of class size in online environments, but this is among the few instances that show a positive correlation to larger class sizes. I suspect this is due to the androgogical implications of studying adult, masters-level students.

Bloom’s 2 sigma problem confronts educators and researchers with the challenge of improving student performance/learning outcomes by 2 sigma based on a combination of 2 or 3 significant variables in instruction, learner, environment, or materials. This semester I am taking Jon Mott’s 1 credit course on the subject, and look forward to finding many enlightening articles and sources, as well as lively and provocative discussion.

I’ve read and though about Bloom’s 2 sigma problem before, but I think on this second read I actually got the point: It’s not that 1-1 tutoring is so potent (it is, but this should be obvious, Oxbridge, apprenticeship models), but that Bloom and his students proved that it’s possible to provoke remarkable improvements in the performance of the average student by altering just one or two variables. This suggests that our understanding of human potential may be misconceived, and that our standard practice of teaching and learning consistently fails to rise above mediocrity.

I’ve heard David Wiley say, why stop at 2 sigma? Why not 3 or 4? Why not indeed? And yet there are so many potentially significant variables in the Bloom study–or any other study that attempts to achieve similar results–that I am naturally cynical of finding a “break through”. (If there had been one already, we would have heard of it, surely?)

A few questions I bring in:
Are the Bloom’s students’ results reliable? repeatable? at least one suggests its not, and without greater details from Bloom et al it’s hard to reproduce the study.

What were the learning outcomes? How deep are they? How important overall to a student’s progress?

What is it about 1-1 that is so useful? Focused and immediate feedback? Q &A? Social aspect? Behavioral?

Should we ignore the 1-1 possibility? Computers, AI have long been thought the possible solution for the human tutoring problem.

Does some 1-1 have a significant effect? Say, 1 hour per week? Could some 1-1 positively affect performance in other areas by (1) motivating, (2) modeling? Say each student in a classroom of 15 gets 30 minutes one-on-one a day in one subject?

How relevant is the 2 sigma problem today? Have our media communications–indeed our culture–changed so much in the past decade that the act of teaching and learning must first be redefined?

We are used to the idea of a bell shaped curve, of low and high achievers. Bloom’s research tweaks that in favor of everyone’s success. As a teacher what narratives do I tell myself to justify student failure?

Definitions and Assumptions

“solitude”…
…is a neutral or positive state of aloneness (“loneliness” is a negative)
…is a form of disconnecting from others, not from information
…in (post)modern American culture (may be) diminishing (through social media)
…has (some) connection to (some kinds of) learning
…may affect some learners more than others
..may be naturally more attractive to some learners than others
…(may be) related to individualism, creativity, self-reliance, independence
…as a mode for learning can be tested and measured through experimental group(s)

The last assumption is the most tenuous, but I might get around to that yet!

Expansion, Rephrasing, and Refinement

(My brainstorming is far looser that those done in class last week.)

What (impact | affect) does solitude have on (learning | e-learning | creativity | attitudes toward learning, e.g. self-reliance)?
Does e-learning occur in more or less solitude than traditional learning?
Is there a historical trend visible in observations of teaching from solitude toward collective/collaborative learning?
How much solitude do modern learners prefer?
What are teachers’ attitudes toward solitary learning?
Is solitude a common observable element in the lives of high-achievers?
Is solitude a correlative to student achievement?
What are the cognitive processes of individuals in solitude?
(Define “solitude”?)
How is learning different from creating?
Is solitude an advantage or disadvantage for informal learning?
(When | why | how) do (solitary | self) learners seek input from (others | experts | peers)?
How do cycles of solitude and group interaction vary amongst different learners?
(Define “different”?)
What environmental design decisions may support learner solitude?
Does (development of | positive attitudes toward | proclivity to) (self-reliance | solitude) increase learner (interaction with | exploration of) (open) educational resources?
<Your question or variant here>

This was my first foray into any sort of concerted examination of this question, so I’m content that the results are all over the map. I don’t know if I’m closer to anything usable here in any pragmatic sense, but I certainly have more to chew on.

As I write these questions my mind can’t help but leap ahead to the question of support for such a study. Who would be interested in finding out the impact of solitude on learning? Organizations with interest in supporting creativity or the arts might be more interested, and so I may reconsider this question