Abstract

Media-Embedded/Embedded-Media (MEEM)

Teaching Techniques and Student Cynicism

Dr. L. L. Morton

University of Windsor

Faculty of Education

401 Sunset Avenue

Windsor, Ontario

N9B3P4

e-mail morton@uwindsor.ca

January 27, 2002

Abstract

Approximately 500 post baccalaureate students were asked to respond to 11 pedagogical techniques to which they were exposed in a media-friendly lecture hall. At times the techniques were embedded in media, at other times the techniques had media embedded within them. A cynicism measure was constructed from those who showed no positive response to a particular method. This led to cynicism rates ranging from 20% to 62% for the various methods used. WEB-oriented methods showed cynicism rates ranging from approximately 28-42%. Ironically, while brief stories (using speech, PowerPoint text and animation) generated the least amount of cynicism (20%), an audio story by a classic story teller generated the most cynicism (62%). A psychodynamic model incorporating information-intake styles, information-expression styles, and demographics was constructed to examine the determinants of such cynicism via Logistic Regression analyses. The model was reliable for six of the 11 variables; and numerous predictor variables revealed the complex interplay between pedagogical technique and the type of student. Even with popular techniques like sound-bites, PowerPoint, animation, MPEG, stories, and the use of the Internet, there was a substantial rate of cynicism. However, in the spirit of "multiple-perspective-taking" these cynicism rates may be viewed as a positive phenomenon as well as a negative phenomenon.

Media-Embedded/Embedded-Media (MEEM)

Pedagogical Techniques and Student Cynicism

The search for pedagogical techniques that will facilitate more effective teaching is a preoccupation of most teachers. The scope of such a search-and-advocacy endeavour is broad, ranging from top-down "direct revelation" of codes (e.g., Moses as legislator, teacher as disseminator) to bottom-up "discovery" (e.g., the hidden codes of Microsoft with Gates as facilitator). Vehicles like revelation, story, dialogue, demonstration, informing, apprenticing, constructing, and "surfing," are found in the educational landscape competing for the teacher’s attention. Thus, we hear expressions like, "What works for you?" "Best Practices!" "What do the ‘Experts’ do?" "I tried this…"

Some experts tell stories, and stories are, indeed, a perennial and popular pedagogical technique—and apparently more so with the ascending postmodern emphasis on narrative and metanarrative. Even quasi-narrative (i.e., little stories) can be "big" techniques in education because they are effective, nuanced, communication vehicles—they inform and entertain. People are motivated to listen to stories. You hear the little story, the true story, of the child who is asked, "What is the thing to do if you see thick smoke coming from your neighbour’s house?" You smile when the child responds, "Call the camera crew from eye witness news" (Flumen & Flumen, 1979, p.83). You smile, but you learned something about our culture and the child’s culture. The story gets the student thinking, and the teacher thinking, about media. Media occupy a high place in our hierarchical cognitive networks. We may be close to the time when Maslow’s hierarchy of needs will need revision to include media as one of the lower level needs.

Some experts see many academic disciplines as forms of storytelling. Postman (1992), for example, sees the work of people like Marx, Weber, Mumford, Jung, Mead, and so on, as storytelling. We learn from their stories. Yet, not everyone agrees that stories are a good pedagogical technique; some would make the case that stories waste time. It is possible that one could communicate in a ten-minute lecture what a story communicates in 100 minutes? So efficiency experts, for example, might challenge the use of story. It is this efficiency mantra that harmonizes best with technology, and invites technique.

Technology, then, becomes the other dominant pedagogical player. It is not necessarily oppositional to "story’ but it is of a different genre. Such items as alphabets, parchment, pencils, printing presses, overhead projectors, audio tapes, radio, TV, and video, have a history presaging educational revolution, in their respective generations. Each time, improving efficiency is an apparent objective and outcome. More recently, it is computers and/or the Internet holding this coveted spot as "the" important pedagogical technique of the day. Advocates like Papert (1980, 1993), Negroponte (1995) and Turkle (1995) make the fascinating case for technology. The more the better! In fact, Papert (1993) uses a biological or evolutionary metaphor when he argues for more and varied technology in the mix, "It is only in such an ecology of mutations and hybridizations of ways of learning that a truly new mathetic culture could emerge" (p.217). Reading such advocates fires the imagination, no doubt, of some! As with stories, though, there are the critics (e.g., Ellul, 1964; Postman, 1992), the concerned (Armstrong & Casement, 1998), and the contrarians (Stoll, 1999). The critiques are credible, but generally muted in the educational milieu.

So there are two sides of each approach—pros and cons whether the art of storytelling (ancient or modern) or the reign of technology (ancient or modern) is the preferred road. The teacher selects. One question, an empirical question, that needs to be addressed for the teacher is: How do students react to these various pedagogical techniques? We can say "everybody loves a story!" But do they? Stories have long been viewed as both entertainment and the great teaching vehicles from our past? They are natural motivators. Children who wanted to read stories wanted to learn to read. Traditions are handed down through stories. Morality is encouraged by the stories of our heroes. Stories make us laugh and weep, cringe and reach. We are enthralled as the wordsmiths craft things of beauty, and joys forever. Surely all students would respond favorably to stories. But do they?

Similarly, others argue for technology as the premier teaching vehicle. The technology provides a tool for accessing facts, databases, systematic information, people, resources and so on. The technology provides multi-sensory input, allows for personal control and provides a real independence. The learners are in a position to season their knowledge plate with the right amounts of text and image—the right amounts for them. Surely all students would respond favorably to technology. But do they?

The extent of cynicism within a student population is one focus of this study. Thus, there is no theoretical critique offered at this point, nor an argument in favour of a particular technique. Rather it is an examination of resident attitudes in a group of university students—attitudes concerning various techniques to which they are exposed.

The particular techniques to be considered are media-based. They are divided in to four general categories (see Table 1), with several techniques in each category.

Table 1: Pedagogical Methods in a Media-Friendly Classroom

What kinds of reactions would students make to these various teaching vehicles? Would one approach be preferable? Moreover, if there are different preferences what would be the characteristics of those who prefer one approach over another?

To examine this question approximately 500 graduate level students rated a variety of pedagogical techniques to which they were exposed. In addition, they were asked to provide information related to preferred intake of information (e.g., concert-going, TV, reading fiction, etc.) as well as preferred output qualities (as reflected in career interests). Together with demographic information (e.g., age major, gender, etc.) these data allowed for consideration of the determinants of interest in various pedagogical strategies.

Method

The subjects for this study were drawn from a population of approximately 760 students, all with at least one undergraduate degree, who were taking an additional year of study to acquire teacher certification and the B.Ed. degree. The responses of 506 students (of the 760 students) led to a return rate of 67%. The characteristics of the sample may be seen in Tables 2-4.

Table 2: Gender Distribution of Sample

Table 3: Age Distribution of Sample

Table 4: Distribution of Majors in the Sample

The instrument to collect the data involved four sections: (1) rating (using a 5-point Likert-type scale) 11 pedagogical strategies/activities (embedded within media, or containing embedded media), to which they had been exposed during one two-hour class (see Table 5 for descriptions), (2) rating on a 5-point scale 17 behavioural activities (e.g., watching drama on TV, reading current fiction, reading the newspaper, etc.), (3) rating on a 5-point scale18 career paths (e.g., drafting, music, accounting, chemistry, etc.) in terms of appeal, and (4) demographic information (e.g., age, major, gender, time-of-day).

Table 5: Pedagogical Techniques During a Two-Hour Class

A media-friendly classroom was used to collect the data. The classroom seats approximately 280 students. It utilizes a large media projector, a fixed computer, a laptop computer (both with direct connections to the Internet), a VCR, an overhead projector, an opaque projector, an integrated sound system, and all of these work seamlessly from a front-and-center control panel. One week following the first class of the year the students were asked to rate the pedagogical activities of the previous week and report on their learning preferences, career interests and personal information. The learning preferences were subjected to a factor analysis using an eigenvalue of 1, varimax rotation, and a loading criterion of .4, with at least two items loading on a factor. This revealed five factors which were termed (1) "Literate-Types," accounting for 21.6% of the variance, (2) "TV-Types," accounting for 13.98% of the variance, (3) "Cultural-Performance-Types," accounting for 8.5% of the variance, (4) "News-Types," accounting for 7.8% of the variance, and (5) "Fiction-Types," accounting for 6.9% of the variance (see Table 6).

Table 6: Information Intake Types

Next, the career preferences were subjected to a factor analysis using an eigenvalue of 1, varimax rotation, and a loading criterion of .4, with at least two items loading on a factor. This revealed five factors which were termed (1) "Business-Types," accounting for 28.1% of the variance, (2) "Tech-Types," accounting for 12.36% of the variance, (3) "Science-Types," accounting for 10.16% of the variance, (4) "Psych-Types," accounting for 6.77% of the variance, and (5) "Arts-Types," accounting for 5.78% of the variance (see Table 7).

Table 7: Information Expression Types

These Information-Intake Types and the Information-Expression Types permitted the construction of a model (see Figure 1) to predict cynical attitudes, and allowed for a fined-grained analysis of determinants of cynicism.

The model involves a "Psychodynamic Approach" to Information Behaviour (Nahl, 2001). In effect, a personality structure is considered as the location for information behaviour and cynicism would be related to (1) affect (negative: uncertainty, confusion, doubt; positive: confidence, self-efficacy, and so on), and (2) cognitive processing preferences (preferential intake styles, and expression or output styles). The model presented in Figure 1 addresses the cognitive components, primarily. The cynicism could be viewed as the affective component. Of interest in this study is the relationship between the two.

Results

Cynicism Rates
To obtain a representation of the attitudes towards the various pedagogical techniques the ratings were collapsed into two categories Non-Cynical and Cynical. The percentages or respondents who were Cynical within each of the 11 techniques was taken as the cynicism measure and then graphed for comparison purposes (see Figure 2).

As may be seen in the figure the least amount of cynicism (approximately 20% of respondents) was expressed with respect to the "Little Stories." Ironically, the most cynicism was expressed with respect to the "Big Story" (approximately 62%). This contrast was a striking aspect of the figure. Techniques that were getting cynical responses from about 40% of the group were the "Nano-lessons," the "WEB Resources," and the "Gimmicks." Techniques that were getting cynical responses from about 30% of the group were the "WEB Outline," the "WEB Tour" and the PowerPoint Presentation of "Research." The technology, generally, rated better than the gimmicks and Nano-lessons, which is a plus for technology. However, the "Little Stories" received the most indicating that technology is not the winner in this race. Remember though, the stories were told with technology—they were embedded in technology and they had technology embedded within them. The mix may make the strongest format.

On the one hand, it is surprising that no one technique appealed to more than 80% of the group, at best. Though these techniques rely heavily on the technology advocated to make learning more palatable to today’s "media generation," there was still a substantial cynicism rate. Moreover, this was the first class in the students’ program—a time when optimism would be expected to be at its peak. Yet the cynicism rates ranged from 20% to 62%.

To explore these cynicism rates further the available data were configured in terms of a model involving information-intake style, demographics, and information-expression style (see Figure 1) and then examined using logistic regression analyses. In these analyses the five input variables, the four demographic variables, and the five output variables were utilized as the model to distinguish between those who liked a particular pedagogical technique as opposed to those who did not.

Music. A test of the full model (the 14 predictor variables) against a constant-only model was not reliable, Χ²(28) = 37.31, p > .05, indicating that the full model did not predict pre-class music-liking. Moreover, an examination of the Wald criterion showed that none of the variables reliably predicted music-liking.

PowerPoint. A test of the full model against a constant-only model was not reliable, Χ²(28) = 37.91, p > .05, indicating that the full model did not predict pre-class PowerPoint liking. An examination of the Wald criterion showed that Gender (Wald = 10.65, p < .01; Odds Ratio = .368) was a reliable predictor (better rating from males), and Fiction-Types (Wald = 3.88, p < .05; Odds Ratio = .757) was a reliable predictor (poorer rating from those liking fiction).

Little-Stories. A test of the full model against a constant-only model was reliable, Χ²(28) = 53.94, p < .01, indicating that the full model did distinguish between those liking Little-Stories and those who didn’t. The variance accounted for was small (Nagelkerke’s R Square = .175). Prediction success for classification showed an overall success rate of 82.1%. An examination of the Wald criterion showed that Gender (Wald = 10.61, p < .01; Odds Ratio = .374) was a reliable predictor (better rating from females).

Sample Projects. Here also a test of the full model was reliable, Χ²(28) = 48.49, p < .01, indicating that the full model did distinguish between those liking Project Samples and those who didn’t (variance accounted for: Nagelkerke’s R Square = .155). Prediction success for classification showed an overall success rate of 80.6%. The Wald criterion showed that Gender (Wald = 6.21, p < .01; Odds Ratio = .467) was a reliable predictor (better rating from females), Time (Wald = 4.65, p < .05; Odds Ratio = .793) was a reliable predictor (better rating earlier in the day), and Kinesiology Major (Wald = 5.16, p < .05; Odds Ratio = .17) was a reliable predictor (poorer rating from Kinesiology Majors).

Nano-lessons. The full model was reliable, Χ²(28) = 49.07, p < .01, distinguishing between those liking Nano-lessons and those who didn’t (variance accounted for: Nagelkerke’s R Square = .14). Prediction success for classification showed an overall success rate of 67.6%. The Wald criterion showed that Science-Types (Wald = 4.88, p < .05; Odds Ratio = 1.297) was a reliable predictor (better rating from Science-Types), Time (Wald = 3.89, p < .05; Odds Ratio = .842) was a reliable predictor (better rating earlier in the day), and Arts-Types (Wald = 5.74, p < .05; Odds Ratio = 1.33) was a reliable predictor (better rating from Arts-Types).

Gimmicks. The full model was not reliable, Χ²(28) = 29.41, p > .05, and therefore, did not distinguish between those liking Gimmicks and those who didn’t. An examination of the Wald criterion showed that Science-Types (Wald = 4.37, p < .01; Odds Ratio = 1.276) was a reliable predictor (better rating from Science-Types).

WEB Outline. The full model was reliable, Χ²(28) = 43.35, p < .05 (variance accounted for: Nagelkerke’s R Square = .127). Prediction success for classification showed an overall success rate of 72%. The Wald criterion showed that Technical-Types (Wald = 3.7, p < .05; Odds Ratio = 1.27) was a reliable predictor (better rating from Technical-Types), Time (Wald = 4.43, p < .05; Odds Ratio = 1.22) was a reliable predictor (better rating earlier in the day), and TV-Types (Wald = 15.23, p < .001; Odds Ratio = 1.6) was a reliable predictor (better rating from TV-Types).

WEB Tour. The full model was reliable, Χ²(28) = 47.79, p < .01 (variance accounted for: Nagelkerke’s R Square = .147). Prediction success for classification showed an overall success rate of 74.9%. The Wald criterion showed that Psychology-Types (Wald = 10.33, p < .001; Odds Ratio = 1.63) was a reliable predictor (better rating from Psychology-Types), and News-Types (Wald = 7.93, p < .01; Odds Ratio = 1.39) was a reliable predictor (better rating from News-Types).

WEB Resources. The full model was reliable, Χ²(28) = 42.65, p < .05 (variance accounted for: Nagelkerke’s R Square = .119). Prediction success for classification revealed an overall success rate of 64%. An examination of the Wald criterion showed that Majors was relevant where Performing-Arts Majors (Wald = 5.84, p < .05; Odds Ratio = 4.36) was a reliable predictor (better rating from Performing-Arts-Types), History Majors (Wald = 7.38, p < .05; Odds Ratio = 5.71) was a reliable predictor (better rating from History Majors), Criminology Majors (Wald = 6.15, p < .05; Odds Ratio = 18.34) was a reliable predictor (better rating from Criminology Majors), Science Majors (Wald = 5.06, p < .05; Odds Ratio = 3.64) was a reliable predictor (better rating from Science Majors), and News-Types (Wald = 4.28, p < .05; Odds Ratio = 1.24) was a reliable predictor (better rating from News-Types).

Research Review. A test of the full model against a constant-only model was not reliable, Χ²(28) = 34.13, p > .05. The Wald criterion showed that History Majors (Wald = 4.25, p < .05; Odds Ratio = 4.42) was a reliable predictor (better rating from History Majors), and Science Majors (Wald = 5.32, p < .05; Odds Ratio = 4.32) was a reliable predictor (better rating from Science Majors).

The "Big" Story. The full model was not reliable, Χ²(28) = 29.44, p > .05. The Wald criterion showed that Literate-Types (Wald = 4.45, p < .05; Odds Ratio = 1.25) was a reliable predictor (better rating from Literate-Types), and Fiction-Types (Wald = 4.55, p < .05; Odds Ratio = 1.27) was a reliable predictor (better rating from Fiction-Types).

Discussion

The most striking aspect of these data is a clear and substantial cynicism rate for a broad range of pedagogical techniques. Even the stories—under the category of Sound-Bites—were viewed in less than positive ways by some (about 20%). This occurred regardless of the optimal conditions associated with the "first class" of the semester, a time when optimism should be high. On the positive side, the least amount of cynicism was expressed with respect to the "Little Stories," and therefore, "story," rather than "technology," would seem to be the more appealing technique for students. But ironically, and more striking, was the cynicism expressed with respect to a classic story teller like W.O. Mitchell. The cynicism rate for this story was 62%. Thus, stories may capture both the high and the low of the cynicism responses. One difference between the two stories was the time allotment (a few minutes versus 25 minutes) which may indicate that a short auditory attention span for today’s adult students is the source of the differenced.

The WEB components also revealed substantial cynicism (28% to 42%). Technological and media-friendly techniques may be appealing to the majority, but it would seem that a large portion of the student population would be somewhat averse to this format. To one who likes media this is both surprising and disappointing. Apparently, one cannot rely, or should not rely, on the technological media even if embedded within stories, or used as vehicles for stories.

Fluctuations were also evident within the WEB techniques: the WEB-Outline and the WEB-Tour were viewed more favourably than the WEB-Resources. This difference is likely related to the personal and immediate implications of the WEB-Outline and the WEB-Tour. These were directly relevant for course requirements, whereas, the WEB-Resources were not immediately relevant. Generally, the pedagogical hope offered by media needs to be tempered by the normal cynicism rates in any student population, as well as such variables as time, relevance, and as may be seem below, the demographic and psychodynamics qualities of the learner.

The model configured to try and get a better understanding of some of the determinants of these attitudes was informative. This Input-Output Psychodynamic Model of Learner-Processing revealed numerous potential determinants of cynical attitudes. The model was viewed as preliminary, exploratory, and a broad ranging configuration of learner-processing, yet it did allow for two levels of analysis and commentary here.

On the one hand, the model was reliable for distinguishing Cynics from Non-Cynics for Sound-Bite Techniques (Little Stories, Project Samples, and Nano-lessons) and WEB Techniques (Outline, Tour, Resources). It was not reliable with respect to the Pre-class Activities or the Traditional Activities. When reliable, however, the amount of variance explained was not large (11.9% to 17.5%), so a tempered response, at best, is warranted.

On the other hand, the specific variables that were reliable predictors are of interest in understanding why there would be differential responses to the pedagogical techniques. This is theoretically interesting, and may lead to better models of learning and better models of teaching. Thus, there is a rationale for considering the responses in terms of Demographics, Input variables and Output variables. That interesting differences exist is not to argue that instructors should try to match pedagogical technique with the learning style of the students; this could be a logistical nightmare. Rather, on the practical side, understanding learner differences could inform instructors about the need for varied methods, could suggest ‘better’ methods, and could create an awareness of the seemingly problematic methods.

In terms of demographics, Gender was a reliable predictor for: (1) the Pre-Class PowerPoint (males were more likely to like it), (2) the Little Stories (females liked those), (3) the Sample Projects (females liked those). So males may gravitate to the visual and the technological, while females are more oriented to story (the story of the "Sample Projects," and the "Little Stories"). This seems to fit our stereotypes.

Time-of-Day was a reliable predictor with more favourable ratings earlier in the day on (1) Nano-lessons, (2) Sample Projects, and (3) the WEB Outline. There seems to have been an attitudinal shift later in the day. What these three techniques may have in common is their function of providing specific information on behavioural demands. If students are more cynical about these later in the day it may be because they are using more cognitively sophisticated processing later in the day. There is a body of literature suggesting enhanced learning later in the day for certain types of tasks (Baddeley, Hatter, Scott & Snashall, Blake, 1967; Folkard, 1979: Folkard & Monk, 1978; Morton, 1986; Morton & Kershner, 1985, 1991; Tilley & Warren, 1983). A more critical attitude, or cynicism, later in the day, is consistent with this literature.

Major was a reliable predictor for Sample Projects (with a poorer rating from Kinesiology Majors). Major was also a reliable predictor for Research (with better ratings—less cynicism—from History Majors and Science Majors). The higher "Research" ratings for History and Science Majors make sense. These majors would be heavily invested in research activity and thus see value in research. The poorer rating from Kinesiology Majors may be logically linked to the action style of this group. Preferring activity they may find some projects too sedentary, or too formal. With respect to the WEB-Resources, the following Majors were reliable predictors: Performing Arts, History, Criminology, and Science (each showing more favourable attitudes). This reveals an interesting and diverse range of background variables that seem to impact the valuing of WEB resources. While History, Criminology and Science majors would logically be drawn to such resources, it is not clear what is drawing the Performing Arts majors.

Fiction-Types was a reliable predictor of the cynicism towards the Pre-Class PowerPoint. They didn’t care for it. But they did like the "Big Story," as did those scoring higher on the Literate-Types scale. This makes sense. Those who like literature especially should be drawn to the sense of story—particularly with a classic storyteller like W.O. Mitchell.

TV-Types predicted WEB-Outline cynicism rate, while News-Types predicted WEB-Tour cynicism rate. That students who like the TV (TV-Types) and news sources (News-Types) would also appreciate the WEB is reasonable. A computer hooked up to the WEB is somewhat homologous with the TV and News sources. Indeed, such a tour could be construed as a form of news media, and similar to watching TV.

Here a number of reliable predictors of cynicism were evident. The Science-Types scale predicted the Nano-lessons cynicism rate, and the Gimmicks cynicism rate. Science-Types liked these sound-bites (the Nano-lessons and the Gimmicks). While these do not map onto the output characteristics mentioned in Table 7 earlier, they do have an associated quality—clear, terse action. This might appeal to "Science-Types."

The Arts-Types scale also predicted the Nano-lessons cynicism rate. One could speculate that the Arts-Types liked the Nano-lessons because they are short and dramatic, and at times have performance elements attached to them.

The Technical-Types scale predicted the WEB-Outline cynicism rate. They liked it. Technical-Types may have a preference for design, structure, technique, and technology.

The Psychology-Types scale predicted the WEB-Tour cynicism rate. Perhaps the Psychology-Types liked the WEB Tour because it provided information they could use in their own outreach. In addition, it was an educational psychology class so the information toured would align with their interests.

Two things are evident here: (1) an apparently complex interplay between the pedagogical technique one experiences, one’s preferred modes of information intake, one’s background, and one’s perceived or preferential output style, and (2) a cynicism with respect to various pedagogical techniques that is influenced by this complex interplay. Even highly rated, and highly valued, techniques have more than their fair share of detractors, and the detractors are interesting in unique ways.

Such information on cynicism may be of value to teachers. Some may wish to utilize techniques associated with lower rates of cynicism. Others may wish to try and match learners and techniques. However, these are not the recommendations being made here. There is a more interesting positive twist that emerges from these findings.

Such information may be of value to administrators. Knowing that there is a residual cynicism rate in a body of university students may temper judgments made of faculty members (regarding renewal, promotion and tenure) which are based on student ratings. But this is not the positive twist either.

The positive twist is the argument that such cynicism is valuable. It is encouraging, and it should be encouraged, cultivated, nurtured and extended. Teachers, perhaps, should be designing their courses to generate more cynicism, not less. The rationale for this argument emerges from (1) empirical studies (e.g., Milgram, 1974), (2) informants (e.g., Garfinkle, 2000; Foucault, 1977; Turkle, 1995), and (3) the critics of technology/technique (e.g., Ellul, 1981; Postman, 1992). With respect to empirical studies, consider Milgram’s famous experiments where subjects were requested to administer electric shock to a person (an accomplice of the experimenter) in response to directives from the authority figure (the researcher). Even when the person receiving the shock appeared to be under great duress many of the subjects continued to administer shock in response to the authority figure’s request. About 35% refused—echoing an interesting rate of cynicism, the cynicism with respect to pedagogy.

With respect to informants, the implication of the surveillance we are exposed to as a function of technology alarms us. We are at risk, and the cynics and critics are a key defence. Thus, when Foucault informs us of the control issues in prisons (and other institutions) as a result of surveillance, we become more cynical. When Turkle (1995) informs us of what is actually happening on the WEB, we become more cynical. When Garfinkle alerts us to the war on privacy by government, business, and neighbours, we call for more cynicism. We see that there are cynics operating in the community and wonder how we can encourage more cynics.

The critic, Ellul, argues that educators need to be teaching students to live "in" technology/technique, but "against" technology/technique. He sees real danger. Whereas some (see Postman, 1992) at least note that the computer is the metaphor of our age—the paradigmatic metaphor—Ellul has a broader vision; for him technology/technique is more than metaphor. The place occupied by "capital" for the past 200 years has now been supplanted by "technology/technique," as Ellul sees it. Power, therefore, has a new substrate; it has shifted to "technology/technique." In addition to this profound social and political effect, there are epistemological effects; he contends that technology/technique has two major epistemological effects—it suppresses the subject, and it suppresses meaning. "The means has entirely replaced the meaning (Ellul, 1980, p.254). Is this what McLuhan meant when contending that the "medium is the message?" The means becomes the end. If he is right, and he makes a good case for this, it is the cynics who will help restore and strengthen the subject, and recover meaning in the presence of an ever widening exercise of "technology/technique." If he is right such cynicism should be cultivated—perhaps developing the self-appointed critic, the cross-examiner, "the devil’s advocate," the Socratic gadfly, the wearer of deBono’s "Black Hat," the contrarian, and so on. The call is out.

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Feedback in Educational Psychology (Correlates for Instructional Formats)

Why Do Some Resist Technology In Classrooms?

Dr. L. L. Morton

University of Windsor

Faculty of Education

401 Sunset Avenue

Windsor, Ontario

N9B3P4

e-mail morton@uwindsor.ca

February 27, 2002

Abstract

Approximately 500 post baccalaureate students were asked to respond to 11 pedagogical techniques to which they were exposed in a media-friendly lecture hall. At times the techniques were embedded in media, at other times the techniques had media embedded within them. The data allowed for students to be grouped into "non-likers" (those who showed no positive response to a particular method) and "likers." This led to non-liking rates ranging from 20% to 62% for the various media-methods used. WEB-oriented methods showed non-liking rates ranging from approximately 28-42%. Ironically, while brief stories (using speech, PowerPoint text and animation) generated the least amount of non-liking (20%), an audio story by a classic story teller generated the most non-liking (62%). While the data collection protocol was weak and adequate psychometric properties of the data collection instrument were not demonstrable the findings are presented here as "quite interesting." Moreover, it was possible to construct a psychodynamic model incorporating information-intake styles, information-expression styles, and demographics to examine the determinants of such liking/non-liking via Logistic Regression analyses. The model was reliable for six of the 11 variables; and numerous predictor variables revealed the complex interplay between pedagogical technique and the type of student. Even with popular techniques like sound-bites, PowerPoint, animation, MPEG, stories, and the use of the Internet, there was a substantial rate of non-liking. However, in the spirit of "multiple-perspective-taking" these non-liking rates may be viewed as a positive phenomenon as well as a negative phenomenon.

Why Do Some Resist Technology In Classrooms?

The search for effective pedagogical practice is a preoccupation of many teachers. The scope of such a search-and-advocacy endeavour is broad, ranging from effective top-down techniques (e.g., presentation, lecture, video, and story) to constructivist bottom-up techniques (e.g., discovery, projects, and creative endeavours). A multiplicity of vehicles like story, dialogue, demonstration, informing, apprenticing, constructing, and "surfing," are found in the educational landscape competing for the teacher’s attention. Thus, we hear expressions like, "What works for you?" "Best Practices!" "What do the ‘Experts’ do?" "I tried this…" "Students don’t like that technique," and so on.

Some might argue that stories are perennial, premier and most popular pedagogical technique—and apparently more so with the ascending postmodern emphasis on narrative and metanarrative. Even quasi-narrative (i.e., little stories) can be "big" techniques in education because they are effective, nuanced, communication vehicles—they inform and entertain. People are motivated to listen to stories. You hear the little story, the true story, of the child who is asked, "What is the thing to do if you see thick smoke coming from your neighbour’s house?" You might smile when the child responds, "Call the camera crew from eye witness news" (Flumen & Flumen, 1979, p.83). You might smile, but you learned something about our culture and the child’s culture. The story gets the student thinking, and the teacher thinking, about media, ironically—the impact of media, the use of media, and the value of media. Media occupy high places in both our cognitive networks and our educational milieu, as does the story.

It is possible to consider many academic disciplines as forms of storytelling. Postman (1992), for example, sees the work of people like Marx, Weber, Mumford, Jung, Mead, and so on, as storytelling. We learn from their stories. Yet, not everyone agrees that stories are a good pedagogical technique; some would make the case that stories waste time. It is possible that one could communicate in a ten-minute lecture what a story communicates in 100 minutes? So efficiency experts, for example, might challenge the use of story. It is this efficiency mantra that harmonizes best with technology, and invites technique. So again, there is this ironic mix of story and technology.

Thus, technology, or technique, becomes another dominant pedagogical player. It is not necessarily oppositional to "story’ but it is of a different genre. Such items as alphabets, parchment, pencils, printing presses, overhead projectors, audio tapes, radio, TV, and video, have a history presaging educational revolution, in their respective generations. Each time, improving efficiency is an apparent objective and outcome. More recently, it is computers and/or the Internet holding this coveted spot as "the" important pedagogical technique of the day. Advocates like Papert (1980, 1993), Negroponte (1995) and Turkle (1995) make the fascinating case for technology. The more the better! In fact, Papert (1993) uses a biological or evolutionary metaphor when he argues for more and varied technology in the mix, "It is only in such an ecology of mutations and hybridizations of ways of learning that a truly new mathetic culture could emerge" (p.217). Thus Papert gains some supporters, and the majority of classrooms get wired-up to the Net. As with stories, though, there are the critics (e.g., Ellul, 1964; Postman, 1992), the concerned (Armstrong & Casement, 1998), and the contrarians (Stoll, 1999). These credible critiques are muted or heard as white noise in the current environment.

So there are two sides with respect to these approaches—pros and cons whether the art of storytelling (ancient or modern) or the reign of technology (ancient or modern) is the preferred road. One question, an empirical question, that arises for the teacher is: How do students react to these various pedagogical techniques? Why do some resist? We could say "everybody loves a story!" But do they? Stories have long been viewed as both entertainment and the great teaching vehicles from our past? They are natural motivators. Children, wanting to read stories, are motivated to learn to read, or so the story goes. Stories supply information. Traditions are handed down through stories. Morality is encouraged by the stories of our heroes. Stories make us laugh and weep, cringe and reach. We are enthralled as the wordsmiths craft things of beauty. Surely all students would respond favorably to stories. But do they?

Yet, others argue for technology as the premier teaching vehicle. The technology provides a tool for accessing facts, databases, systematic information, people, resources and so on. The technology provides multi-sensory input, allows for personal control and provides a real independence. The learners are in a position to season their knowledge plate with the right amounts of text and image, substance and appetizer—the right amounts for them. All students respond favorably to technology! But do they?

The extent of such liking/non-liking within a student population is a focus of this present study. Thus, there is no theoretical critique offered at this point, nor an argument in favour of a particular technique. Rather it is an examination of resident attitudes in a group of university students—attitudes concerning various pedagogical techniques to which they are exposed. The particular techniques to be considered are media-based. They are divided into four general categories (see Table 1), with several techniques in each category.

Table 1: Pedagogical Methods in a Media-Friendly Classroom

What kinds of reactions would students show to these various teaching vehicles? Would one approach be more preferable? Moreover, if there are different preferences what would be the characteristics of those who prefer one approach over another?

The opportunity to examine this question emerged somewhat serendipitously. Having noticed a great deal of animosity towards a particular classroom activity in one class the situation was explored further by having approximately 500 post-baccalaureate level students simply give a rating for a variety of pedagogical techniques to which they were exposed in a class. In addition, in an attempt to explore some of the determinants of such animosity, they were asked to provide ratings related to different information-intake formats (e.g., concert-going, TV, reading fiction, etc.) as well as preferred output qualities (as reflected in career interests). Together with demographic information (e.g., age major, gender, etc.) these data allowed for at least a preliminary consideration of some of the determinants of interest in various pedagogical strategies.

Method

The subjects for this study were drawn from a population of approximately 760 students, all with at least one undergraduate degree, who were taking an additional year of study to acquire teacher certification and the B.Ed. degree. The responses of 506 students (of the 760 students) led to a return rate of 67%. Demographic characteristics of the sample may be seen in Tables 2-4.

Table 2: Gender Distribution of Sample

Table 3: Age Distribution of Sample

Table 4: Distribution of Majors in the Sample

The instrument to collect the data involved four sections: (1) rating (using a 5-point Likert-type scale) 11 pedagogical strategies/activities (linked to media), to which they had been exposed during one two-hour class (see Table 5 for descriptions), (2) rating on a 5-point scale 17 behavioural activities (e.g., watching drama on TV, reading current fiction, reading the newspaper, etc.), (3) rating on a 5-point scale18 career paths (e.g., drafting, music, accounting, chemistry, etc.) in terms of appeal, and (4) demographic information (e.g., age, major, gender, time-of-day).

Table 5: Pedagogical Techniques During a Two-Hour Class

The instruction occurred in a media-friendly classroom. The classroom seats approximately 280 students. It utilizes a large media projector, a fixed computer, a laptop computer (both with direct connections to the Internet), a VCR, an overhead projector, an opaque projector, an integrated sound system, and all of these work seamlessly from a front-and-center control panel. One week following the first class of the year the students were asked to reflect on the pedagogical activities of the previous week and then rate them. They were also asked to report on some information-intake activities, career interests and personal information. While the initial intent was to gather information that could be relevant for instructional practices there was a suspicion that something interesting might emerge from these data, and therefore the protocol was submitted to an ethics committee for review, a "just-in-case" strategy.

The information-intake activities were subjected to a factor analysis using an eigenvalue of 1, varimax rotation, and a loading criterion of .4, with at least two items loading on a factor. This revealed five factors which were termed (1) "Literate-Types," accounting for 21.6% of the variance, (2) "TV-Types," accounting for 13.98% of the variance, (3) "Cultural-Performance-Types," accounting for 8.5% of the variance, (4) "News-Types," accounting for 7.8% of the variance, and (5) "Fiction-Types," accounting for 6.9% of the variance (see Table 6). The sample was split in half and two additional factor analyses were run (one on each sample, with the second as a confirmatory factor analysis) and the same five types emerged in both analyses.

Table 6: Information Intake Types

Next, the career preferences were subjected to a factor analysis using the same criteria as the former factor analyses. This revealed five factors which were termed (1) "Business-Types," accounting for 28.1% of the variance, (2) "Tech-Types," accounting for 12.36% of the variance, (3) "Science-Types," accounting for 10.16% of the variance, (4) "Psychology-Types," accounting for 6.77% of the variance, and (5) "Arts-Types," accounting for 5.78% of the variance (see Table 7). Again the sample was split in half and two additional factor analyses were run (one on each sample, with the second as a confirmatory factor analysis) and the same five types were evident in both analyses.

Table 7: Information Expression Types

These Information-Intake Types and the Information-Expression Types permitted the construction of a model (see Figure 1) to predict the "non-liking" attitudes, and allowed for a fined-grained analysis of determinants of non-liking.

The model involves a "Psychodynamic Approach" to Information Behaviour (Nahl, 2001). In effect, a personality structure is considered as the location for information processing behaviour and "liking/non-liking" would be related to (1) affect (negative: uncertainty, confusion, doubt; positive: confidence, self-efficacy, and so on), and (2) cognitive processing preferences (preferential intake styles, and expression or output styles). The model presented in Figure 1 addresses the cognitive components, primarily. The "non-liking" could be viewed as the affective component. Of interest in this study is the relationship between the two—the affective and the cognitive.

Results

Non-liking Rates
To obtain a representation of the attitudes towards the various pedagogical techniques the ratings were collapsed into two categories: Non-likers and Likers. The percentages or respondents who were Non-likers within each of the 11 techniques was taken as the non-liking measure and then graphed for comparison purposes (see Figure 2).

As may be seen in the figure the least amount of non-liking (approximately 20% of respondents) was expressed with respect to the "Little Stories." Ironically, the most non-liking was expressed with respect to the "Big Story" (approximately 62%). This contrast was a striking aspect of the figure. Techniques that were getting non-liking responses from about 40% of the group were the "Nano-lessons," the "WEB Resources," and the "Gimmicks." Techniques that were getting non-liking responses from about 30% of the group were the "WEB Outline," the "WEB Tour" and the PowerPoint Presentation of "Research." The technology, generally, fared better than the gimmicks and Nano-lessons, which is a plus for technology. However, the "Little Stories" received the most support indicating that technology is not the winner in this race. Remember though, the stories were told with technology—they were embedded in technology and they had technology embedded within them. The mix may make the strongest format.

On the one hand, it is surprising that no one technique appealed to more than 80% of the group, at best. Though these techniques rely heavily on the technology advocated to make learning more palatable to today’s "media generation," there was still a substantial non-liking rate. Moreover, this was the first class in the students’ program—a time when optimism would be expected to be at its peak. Yet the non-liking rates ranged from 20% to 62%.

To explore these non-liking rates further the available data were configured in terms of a model involving information-intake style, demographics, and information-expression style (see Figure 1) and then examined using logistic regression analyses. In these analyses the five input variables, the four demographic variables, and the five output variables were utilized as the model to distinguish between those who liked a particular pedagogical technique as opposed to those who did not.

Music. A test of the full model (the 14 predictor variables) against a constant-only model was not reliable, Χ²(28) = 37.31, p > .05, indicating that the full model did not predict pre-class music-liking. Moreover, an examination of the Wald criterion showed that none of the variables reliably predicted music-liking.

Pre-Class PowerPoint. A test of the full model against a constant-only model was not reliable, Χ²(28) = 37.91, p > .05, indicating that the full model did not predict pre-class PowerPoint liking. An examination of the Wald criterion showed that Gender (Wald = 10.65, p < .01; Odds Ratio = .368) was a reliable predictor (better rating from males), and Fiction-Types (Wald = 3.88, p < .05; Odds Ratio = .757) was a reliable predictor (poorer rating from those liking fiction).

Little-Stories. A test of the full model against a constant-only model was reliable, Χ²(28) = 53.94, p < .01, indicating that the full model did distinguish between those liking Little-Stories and those who didn’t. The variance accounted for was small (Nagelkerke’s R Square = .175). Prediction success for classification showed an overall success rate of 82.1%. An examination of the Wald criterion showed that Gender (Wald = 10.61, p < .01; Odds Ratio = .374) was a reliable predictor (better rating from females).

Sample Projects. Here also a test of the full model was reliable, Χ²(28) = 48.49, p < .01, indicating that the full model did distinguish between those liking Project Samples and those who didn’t (variance accounted for: Nagelkerke’s R Square = .155). Prediction success for classification showed an overall success rate of 80.6%. The Wald criterion showed that Gender (Wald = 6.21, p < .01; Odds Ratio = .467) was a reliable predictor (better rating from females), Time (Wald = 4.65, p < .05; Odds Ratio = .793) was a reliable predictor (better rating earlier in the day), and Kinesiology Major (Wald = 5.16, p < .05; Odds Ratio = .17) was a reliable predictor (poorer rating from Kinesiology Majors).

Nano-lessons. The full model was reliable, Χ²(28) = 49.07, p < .01, distinguishing between those liking Nano-lessons and those who didn’t (variance accounted for: Nagelkerke’s R Square = .14). Prediction success for classification showed an overall success rate of 67.6%. The Wald criterion showed that Science-Types (Wald = 4.88, p < .05; Odds Ratio = 1.297) was a reliable predictor (better rating from Science-Types), Time (Wald = 3.89, p < .05; Odds Ratio = .842) was a reliable predictor (better rating earlier in the day), and Arts-Types (Wald = 5.74, p < .05; Odds Ratio = 1.33) was a reliable predictor (better rating from Arts-Types).

Gimmicks. The full model was not reliable, Χ²(28) = 29.41, p > .05, and therefore, did not distinguish between those liking Gimmicks and those who didn’t. An examination of the Wald criterion showed that Science-Types (Wald = 4.37, p < .01; Odds Ratio = 1.276) was a reliable predictor (better rating from Science-Types).

WEB Outline. The full model was reliable, Χ²(28) = 43.35, p < .05 (variance accounted for: Nagelkerke’s R Square = .127). Prediction success for classification showed an overall success rate of 72%. The Wald criterion showed that Technical-Types (Wald = 3.7, p < .05; Odds Ratio = 1.27) was a reliable predictor (better rating from Technical-Types), Time (Wald = 4.43, p < .05; Odds Ratio = 1.22) was a reliable predictor (better rating earlier in the day), and TV-Types (Wald = 15.23, p < .001; Odds Ratio = 1.6) was a reliable predictor (better rating from TV-Types).

WEB Tour. The full model was reliable, Χ²(28) = 47.79, p < .01 (variance accounted for: Nagelkerke’s R Square = .147). Prediction success for classification showed an overall success rate of 74.9%. The Wald criterion showed that Psychology-Types (Wald = 10.33, p < .001; Odds Ratio = 1.63) was a reliable predictor (better rating from Psychology-Types), and News-Types (Wald = 7.93, p < .01; Odds Ratio = 1.39) was a reliable predictor (better rating from News-Types).

WEB Resources. The full model was reliable, Χ²(28) = 42.65, p < .05 (variance accounted for: Nagelkerke’s R Square = .119). Prediction success for classification revealed an overall success rate of 64%. An examination of the Wald criterion showed that Majors was relevant where Performing-Arts Majors (Wald = 5.84, p < .05; Odds Ratio = 4.36) was a reliable predictor (better rating from Performing-Arts-Types), History Majors (Wald = 7.38, p < .05; Odds Ratio = 5.71) was a reliable predictor (better rating from History Majors), Criminology Majors (Wald = 6.15, p < .05; Odds Ratio = 18.34) was a reliable predictor (better rating from Criminology Majors), Science Majors (Wald = 5.06, p < .05; Odds Ratio = 3.64) was a reliable predictor (better rating from Science Majors), and News-Types (Wald = 4.28, p < .05; Odds Ratio = 1.24) was a reliable predictor (better rating from News-Types).

Research Review. A test of the full model against a constant-only model was not reliable, Χ²(28) = 34.13, p > .05. The Wald criterion showed that History Majors (Wald = 4.25, p < .05; Odds Ratio = 4.42) was a reliable predictor (better rating from History Majors), and Science Majors (Wald = 5.32, p < .05; Odds Ratio = 4.32) was a reliable predictor (better rating from Science Majors).

The "Big" Story. The full model was not reliable, Χ²(28) = 29.44, p > .05. The Wald criterion showed that Literate-Types (Wald = 4.45, p < .05; Odds Ratio = 1.25) was a reliable predictor (better rating from Literate-Types), and Fiction-Types (Wald = 4.55, p < .05; Odds Ratio = 1.27) was a reliable predictor (better rating from Fiction-Types).

Discussion

The most striking aspect of these data is a clear and substantial non-liking rate for a broad range of pedagogical techniques. Even the stories—under the category of Sound-Bites—were viewed in less than positive ways by some (about 20%). This occurred regardless of the optimal conditions associated with the "first class" of the semester, a time when optimism should be high. On the positive side, the least amount of non-liking was expressed with respect to the "Little Stories," and therefore, "story," rather than "technology," would seem to be the more appealing technique for students. But ironically, and more striking, was the non-liking expressed with respect to a classic story teller like W.O. Mitchell. The non-liking rate for this story was 62%. It seems stories may capture both the high and the low of the non-liking responses. One difference between the two stories was the time allotment (a few minutes versus 25 minutes) which may indicate that a short auditory attention span for today’s adult students is a possible source of the difference.

The WEB components also revealed substantial non-liking (28% to 42%). Technological and media-friendly techniques may be appealing to the majority, but it would seem that a large portion of the student population would be somewhat averse to this format. To one who likes media this is both surprising and disappointing. Apparently, one cannot rely, or should not rely, on the technological media even if embedded within stories, or used as vehicles for stories.

Fluctuations were also evident within the WEB techniques: the WEB-Outline and the WEB-Tour were viewed more favourably than the WEB-Resources. This difference is likely related to the personal and immediate implications of the WEB-Outline and the WEB-Tour. These were directly relevant for course requirements, whereas, the WEB-Resources were not "immediately" relevant, and thus the lower rating for WEB-Resources. Generally, the pedagogical hope offered by media needs to be tempered by the normal non-liking rates in any student population, as well as such variables as time, relevance, and, as may be seen below, the demographic and psychodynamic qualities of the learner.

The model configured to try and get a better understanding of some of the determinants of these attitudes was informative. This Input-Output Psychodynamic Model of Learner-Processing revealed numerous potential determinants of non-liking attitudes. The model was viewed as preliminary, exploratory, and a broad ranging configuration of learner-processing, yet it did allow for two levels of analysis and commentary here.

On the one hand, the model was reliable for distinguishing Likers from Non-Likers for Sound-Bite Techniques (Little Stories, Project Samples, and Nano-lessons) and WEB Techniques (Outline, Tour, Resources). It was not reliable with respect to the Pre-class Activities or the Traditional Activities. When reliable, however, the amount of variance explained was not large (11.9% to 17.5%), so a tempered response, at best, is warranted.

On the other hand, the specific variables that were reliable predictors are of interest in understanding why there would be differential responses to the pedagogical techniques. This is theoretically interesting, and may lead to more refined models of learning and teaching. Thus, there is a rationale for considering the responses in terms of Demographics, Input variables and Output variables. That interesting differences exist is not to argue that instructors should try to match pedagogical technique with the learning style of the students; this could be a logistical nightmare. Rather, on the practical side, understanding learner differences could inform instructors about the need for varied methods, could suggest ‘better’ methods, and could create an awareness of the seemingly problematic methods.

In terms of demographics, Gender was a reliable predictor for: (1) the Pre-Class PowerPoint (males were more likely to like it), (2) the Little Stories (females liked those), and (3) the Sample Projects (females liked those). So males may gravitate to the visual and the technological, while females are more oriented to story (the story of the "Sample Projects," and the "Little Stories"). This seems to fit our stereotypes.

Time-of-Day was a reliable predictor with more favourable ratings earlier in the day on (1) Nano-lessons, (2) Sample Projects, and (3) the WEB Outline. There seems to be an attitudinal shift later in the day. What these three techniques may have in common is their function of providing specific information on behavioural demands. If students are more critical of these later in the day it may be because they are using more cognitively sophisticated processing later in the day. There is a body of literature suggesting enhanced learning later in the day for certain types of tasks (Baddeley, Hatter, Scott & Snashall, Blake, 1967; Folkard, 1979: Folkard & Monk, 1978; Morton, 1986; Morton & Kershner, 1985, 1991; Tilley & Warren, 1983). A more critical attitude, which may be linked to non-liking, is consistent with this time-of-day literature.

Major was a reliable predictor for Sample Projects (with a poorer rating from Kinesiology Majors). Major was also a reliable predictor for Research (with better ratings—less non-liking—from History Majors and Science Majors). The higher "Research" ratings for History and Science Majors make sense. These majors would be heavily invested in research activity and thus see value in research. The poorer rating from Kinesiology Majors may be logically linked to the action style of this group. Preferring activity they may find some projects too sedentary, or too formal. With respect to the WEB-Resources, the following Majors were reliable predictors: Performing Arts, History, Criminology, and Science (each showing more favourable attitudes). This reveals an interesting and diverse range of background variables that seem to impact the valuing of WEB resources. While History, Criminology and Science majors would logically be drawn to such resources, it is not clear what is drawing the Performing Arts majors.

Fiction-Types was a reliable predictor of the non-liking towards the Pre-Class PowerPoint. They didn’t care for it. But they did like the "Big Story," as did those scoring higher on the Literate-Types scale. This makes sense. Those who like literature especially should be drawn to the sense of story—particularly with a classic storyteller like W.O. Mitchell.

TV-Types predicted WEB-Outline non-liking rate, while News-Types predicted WEB-Tour non-liking rate. That students who like the TV (TV-Types) and news sources (News-Types) would also appreciate the WEB is reasonable. A computer hooked up to the WEB is somewhat homologous with the TV and News sources. Indeed, such a tour could be construed as a form of news media, and similar to watching TV.

Here a number of reliable predictors of non-liking were evident. The Science-Types scale predicted the Nano-lessons non-liking rate, and the Gimmicks non-liking rate. Science-Types liked these sound-bites (the Nano-lessons and the Gimmicks). While these do not map onto the output characteristics mentioned in Table 7 earlier, they do have an associated quality—clear, terse action. This might appeal to "Science-Types."

The Arts-Types scale also predicted the Nano-lessons non-liking rate. One could speculate that the Arts-Types liked the Nano-lessons because they are short and dramatic, and at times have performance elements attached to them.

The Technical-Types scale predicted the WEB-Outline non-liking rate. They liked it. Technical-Types may have a preference for design, structure, technique, and technology.

The Psychology-Types scale predicted the WEB-Tour non-liking rate. Perhaps the Psychology-Types liked the WEB Tour because it provided information they could use in their own outreach. In addition, it was an educational psychology class so the information toured would align with their interests.

Two things are evident here: (1) an apparently complex interplay between the pedagogical technique one experiences, one’s preferred modes of information intake, one’s background, and one’s perceived or preferential output style, and (2) a non-liking with respect to various pedagogical techniques that is influenced by this complex interplay. Even highly rated, and highly valued, techniques have more than their fair share of detractors, and the detractors are interesting in unique ways. Thus an answer to the question "Why do some resist technology in the classroom?" would emerge as: (1) there is a fluctuating, but striking, resident "non-liking" rate for any particular technique in a large group, and (2) there are logical psychodynamic and experiential determinants that influence "liking/non-liking."

Such information on non-liking may be of value to teachers. Some may wish to utilize techniques associated with lower rates of non-liking. Others may wish to try and match learners and techniques. However, these are not the recommendations of interest here; yet there is an interesting positive twist that emerges from these findings.

Such information may be of value to administrators. Knowing that there is a residual non-liking rate in a body of university students may temper judgments made of faculty members (regarding renewal, promotion and tenure) which are based on student ratings. But this is not the positive twist either.

The positive twist is the argument that such non-liking is valuable. It is encouraging, and it should be encouraged, cultivated, nurtured and extended. Teachers, perhaps, should be designing their courses to generate more non-liking, not less. The rationale for this argument emerges from (1) empirical studies (e.g., Milgram, 1974), (2) informants (e.g., Garfinkle, 2000; Turkle, 1995), and (3) the critics of technology/technique (e.g., Ellul, 1981; Postman, 1992). With respect to empirical studies, consider Milgram’s famous experiments where subjects were requested to administer electric shock to a person (an accomplice of the experimenter) in response to directives from the authority figure (the researcher). Even when the person receiving the shock appeared to be under great duress many of the subjects continued to administer shock in response to the authority figure’s request. About 35% refused—echoing an interesting rate of non-liking—a similar rate as the non-liking with respect to pedagogy.

With respect to informants, the implication of the surveillance we are exposed to as a function of technology alarms us. We are at risk, and the critics are a key defence. When Turkle (1995) informs us of what is actually happening on the WEB, we become more suspicious and welcome those who adopt the more cynical view. When Garfinkle alerts us to the war on privacy by government, business, and neighbours, we call for more critical awareness. We see that there are critics operating in the community and wonder how we can encourage the more critical and the more cynical view in our students.

One critic, Ellul, argues that educators need to be teaching students to live "in" technology/technique, but "against" technology/technique. He sees "real" danger. Whereas some (see Postman, 1992) at least note that the computer is the metaphor of our age—the paradigmatic metaphor—Ellul has a broader vision; for him technology/technique is more than metaphor. The place occupied by "capital" for the past 200 years has now been supplanted by "technology/technique," as Ellul sees it. Power, therefore, has a new substrate; it has shifted to "technology/technique." In addition to this profound social and political effect, there are epistemological effects; he contends that technology/technique has two major epistemological effects—it suppresses the subject, and it suppresses meaning. "The means has entirely replaced the meaning" (Ellul, 1980, p.254). Is this what McLuhan meant when contending that the "medium is the message?" The means becomes the end. If he is right, and he makes a good case for this, it is the critics—the cynics—who will help restore and strengthen the subject, and recover meaning in the presence of an ever widening exercise of "technology/technique." If he is right such resident non-liking, or critical attitude, or cynicism, should be cultivated—perhaps developing the self-appointed critic, the cross-examiner, "the devil’s advocate," the Socratic gadfly, the wearer of deBono’s "Black Hat," the contrarian, and so on.

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