October 2012 Volume 15 Number 4 - Educational Technology ...

0.594; Kruskal-Wallis, H = 0.74, DF = 3, P = 0.865), INT(Levene Statistic=0.54, p=0.657; Kruskal-Wallis H = 1.21,
DF = 3, P = 0.751), AT(Levene Statistic=1.27,p=0.302; Kruskal-Wallis H = 2.30, DF = 3,P = 0.512). This means
that there was no obvious relationship between adoption variables across the various classes of teachers with respect
to quality of posed questions. There was no difference in intention to adopt even for teachers in the top quartile 25%
with respect to high quality questions (Levene Statistic=0.69, p=0.534; Kruskal-Wallis H=0.11, DF=2, P=0.948).
Limitations
The results are limited to the number of teachers, their distribution in the country, topics and type of questions posed.
The study also used a very simple and limited adoption model based on TAM. Many expanded variants and
especially the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al., 2003) can be used
for subsequent investigations. Table 3 presents an incomplete list of potential factors that have an impact on adoption
of knowledge sharing (and withholding) in communities of practice. It is important to contextualize the adoption
model because some factors like Reciprocity are particularly relevant for Wikis. Other factors like Self-efficacy, on
the other hand, are important for adoption of educational Wikis (Liu, 2010) but have been de-emphasized in generic
models like UTAUT.
Table 3. Potential factors for inclusion in an expanded adoption model for assessment Wikis
Factor Study
Benevolence (Yang & Laim, 2010), (Lin & Huang, 2009)
Self-efficacy (Wang & Noe, 2010),(Kim, Song & Jones, 2010), (Chen & Hung,
2010),(Liu, 2010) (Tsai & Cheng, 2010)
Trust (Wang & Noe, 2010), (Chiu, Hsu, & Wang, 2006), (Lin, Hung &
Chen, 2009), (Chen & Hung, 2010), (Zhanga, Fang, Weib, & Chenc,
2010), (Hsua, Jub, Yenc, & Changa, 2007)
Reciprocity (Chiu, Hsu, & Wang, 2006), (Chen & Hung, 2010)
Fair reward (Lin, Hung & Chen, 2009)
Enthusiasm and persistence (Lin, & Huang, 2008)
Empathy (Lin & Huang, 2009)
Community identification (Hsu & Lin, 2008)
“Lead user” characteristics (Jeppesena & Laursenb, 2009)
Team characteristics (e.g., diversity), (Wang & Noe, 2010), (Lin & Huang , 2008)
Cultural characteristics (e.g., collectivism) (Wang & Noe, 2010),(Tsai & Cheng, 2010)
Discussion
Within experimental limits, the study shows that teachers in a developing country like Pakistan are able to formulate
adequate multiple-choice questions tied to student learning outcomes without any specialized training. What is
relevant to crowd sourcing is that the top quartile of these teachers was able to formulate excellent questions.
However, it is interesting to note that teachers from seemingly backwards areas of the country like Nowshera and
Peshawar, where the teacher training programs are also lacking, were able to contribute better questions than those
from the urban center of Karachi. In addition, No relationship was found between a teacher’s willingness to adopt the
assessment creation process and an ability to pose good questions. This data support one of the key strengths and the
potential weakness of crowd sourcing; it is very difficult to predict where the best contributors will come from.
While teachers who formulated better questions came from public schools and smaller cities, teacher training seemed
not to have had an impact on the quality of questions which is surprising and needs to be explored; are teachers being
trained adequately in problem posing? One explanation of why public school teachers performed better may be the
plethora of foreign funding agency teacher training programs (like USAID and DFID) that typically target
government schools.
The teachers, who had the highest willingness to adopt assessment Wiki, also enjoyed the process, found the process
easy to use, and had a positive attitude towards contributing assessments. Usefulness, however, was not a
distinguishing factor. In other words, teachers who were willing to adopt crowd sourcing did not necessarily believe
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