ArticlePDF Available

Abstract and Figures

Effective teaching of engineering concepts relies both on carefully designed lesson plans that meet specific learning outcomes, and on classroom activities that students find engaging. Without student engagement, even the best designed plans will fail to meet their outcomes. In other words, students need to be actively involved in the learning process. The objective of this paper is to present a case study of applying a novel active learning method, specifically educational prediction markets (EPM), for teaching project management classes at a major research university. This method was investigated for its effectiveness in engaging students, as well as promoting learning of probabilistic reasoning without explicit teaching. Student surveys, following the EPM implementation, revealed both advantages and disadvantages. The two key benefits reported by the students were: a) providing better connections between the materials taught in the class and realities of construction projects, and b) increasing overall interest and enthusiasm in learning about project risk management due to the game-like nature of the process. The main disadvantage was disengagement by a subset of students due to perceptions that fellow students were manipulating the market results.
Content may be subject to copyright.
PostedonReseachGate
PredictionMarketsasa
TeachingToolFor
UndergraduateEngineering
Students
Damnjanovic,I.;Faghihi,Vahid;Scott,C.;McTigue,E.;and
Reinschmidt,K.
JournalofProfessionalIssuesinEngineeringEducationandPractice‐SubmittedJune21,2011;accepted
May22,2012;postedaheadofprintMay24,2012.doi:10.1061/(ASCE)EI.19435541.0000127

Accepted Manuscript
Not Copyedited
1
EDUCATIONAL PREDICTION MARKETS: A CONSTRUCTION PROJECT
MANAGEMENT CASE STUDY
Ivan Damnjanovic1, Vahid Faghihi2, Chyllis Scott3, Erin McTigue4, and Kenneth Reinschmidt5
ABSTRACT
Effective teaching of engineering concepts relies both on carefully designed lesson plans that
meet specific learning outcomes, and on classroom activities that students find engaging.
Without student engagement, even the best designed plans will fail to meet their outcomes. In
other words, students need to be actively involved in the learning process. The objective of this
paper is to present a case study of applying a novel active learning method, specifically
educational prediction markets (EPM), for teaching project management classes at a major
research university. This method was investigated for its effectiveness in engaging students, as
well as promoting learning of probabilistic reasoning without explicit teaching. Student surveys,
following the EPM implementation, revealed both advantages and disadvantages. The two key
benefits reported by the students were: a) providing better connections between the materials
taught in the class and realities of construction projects, and b) increasing overall interest and
enthusiasm in learning about project risk management due to the game-like nature of the process.
The main disadvantage was disengagement by a subset of students due to perceptions that fellow
students were manipulating the market results.
1 Assistant Professor, Texas A&M University, Zachry Department of Civil Engineering, College Station, TX 77843-3136, E-
mail: idamnjanovic@civil.tamu.edu
2 Ph.D. candidate, Texas A&M University, Zachry Department of Civil Engineering, College Station, TX 77843-3136; E-mail:
savafa@tamu.edu
3 Ph.D. candidate, Texas A&M University, Department of Teaching Learning and Culture, College Station, TX 77843, E-mail:
chyllisscott@tamu.edu
4 Assistant Professor, Texas A&M University, Department of Teaching Learning and Culture, College Station, TX 77843, E-
mail: emtigue@tamu.edu
5 J. L. Frank/Marathon Ashland Petroleum LLC Chair in Engineering Project Management, Zachry Department of Civil
Engineering, College Station, TX 77843-3136, Email: kreinschmidt@civil.tamu.edu
Journal of Professional Issues in Engineering Education and Practice. Submitted June 21, 2011; accepted May 22, 2012;
posted ahead of print May 24, 2012. doi:10.1061/(ASCE)EI.1943-5541.0000127
Copyright 2012 by the American Society of Civil Engineers
J. Prof. Issues Eng. Educ. Pract.
Downloaded from ascelibrary.org by Texas A&M Univ College Station on 11/01/12. Copyright ASCE. For personal use only; all rights reserved.
Accepted Manuscript
Not Copyedited
2
INTRODUCTION
Teaching how to identify, assess, and manage project risks presents many challenges. One of the
greatest challenges instructors face is the inherent difficulty of linking probabilistic predictions
with actual observations. For example, we can predict that the probability of rain tomorrow is 80
percent, but then, it may not rain. Was our prediction good? It is difficult to answer this question
because predictions are only probabilistic propositions, not deterministic observations.
This conundrum of teaching probability concepts is particularly visible when trying to predict
outcomes such as completion time of construction projects. While some engineering disciplines
can use laboratory experiments to validate the uncertainty in prediction, this approach cannot be
applied to construction management. For example, we can run 100 laboratory tests to determine
the probability that a concrete sample taken from Batch A will withstand the stresses required by
seismic codes. This empirical approach to determining risk is visible and obvious, even for
novices to the field such as undergraduate students. However, for complex projects, where one
cannot directly observe outcomes and estimate probabilities - how does one assess a probabilistic
prediction?
In teaching project risks instructors often rely on numerical methods, such as Monte Carlo
simulations (MCS). Even though simulations may be more transparent than other methods, they
can be considered susceptible to manipulation by the simulator. In our teaching experience,
students often view the MCS approach as too abstract, which in turn can make students
suspicious and disengaged from further exploration. This feedback loop - where a lack of realism
diminishes student engagement; and deficiency in engagement prevents further investigation to
understand abstract concepts - represents a major hurdle in teaching engineering project risk
Journal of Professional Issues in Engineering Education and Practice. Submitted June 21, 2011; accepted May 22, 2012;
posted ahead of print May 24, 2012. doi:10.1061/(ASCE)EI.1943-5541.0000127
Copyright 2012 by the American Society of Civil Engineers
J. Prof. Issues Eng. Educ. Pract.
Downloaded from ascelibrary.org by Texas A&M Univ College Station on 11/01/12. Copyright ASCE. For personal use only; all rights reserved.
Accepted Manuscript
Not Copyedited
3
management. Until we gain students’ attention and engagement by grounding the learning within
real world examples, the learning process is stalled.
The objective of this case study is to document implementation of EPM in undergraduate project
management classes. For background, prediction markets have recently found applications in
many fields including education, project management and scientific research (Hanson 1999,
Arrow 2008). In a prediction market a participant buys or sells shares in the realization of a
specific well-defined outcome. If the predicted outcome occurs, he/she can exchange the shares
for a “reward” of 100 units per share. If the predicted outcome doesn’t occur, the value of the
shares becomes 0. If a particular outcome is likely, the price of shares will go up (as demand
grows) and vice versa, as the specified outcome seems less likely to happen, the market price
will go down. Hence, prediction markets represent the social trade forums that run for the
primary purpose of aggregating information in an effort to forecast future events (Tziralis &
Tatsiopoulos 2007; Berg & Rietz 2003; Berg et al. 2003). Arguably, the most important issue
with implementation of a market is its performance as a predictive tool (Wolfers and Zitzewitz,
2004). On a practical note, in EPM these prices and rewards do not constitute illegal gambling
because no money is exchanged. Nevertheless, participants are motivated and engaged by non-
monetary rewards, particularly due to the competitive nature of games.
To investigate the extent to which EPM could promote engagement, active learning, and the
sense of realism in teaching, we recently implemented an EPM contest in two undergraduate
construction engineering and management classes focusing on project risk management. We
collected and analyzed feedback data including qualitative observations of the learning process.
This manuscript documents our findings and sets EPM in a larger context of using active
learning methods.
Journal of Professional Issues in Engineering Education and Practice. Submitted June 21, 2011; accepted May 22, 2012;
posted ahead of print May 24, 2012. doi:10.1061/(ASCE)EI.1943-5541.0000127
Copyright 2012 by the American Society of Civil Engineers
J. Prof. Issues Eng. Educ. Pract.
Downloaded from ascelibrary.org by Texas A&M Univ College Station on 11/01/12. Copyright ASCE. For personal use only; all rights reserved.
Accepted Manuscript
Not Copyedited
4
The remainder of the paper is organized as follows. First we provide a brief background on
education theories that support the use of EPM. Next, we present the case study:the synopsis of
the overall process, the data collection process, the results from the survey, and the interpretation
of the findings. Finally, we present lessons learned and summary and directions for future
studies.
BACKGROUND
Active Learning (AL) is often referred to as active engagement strategies that allow students to
participate and engage in higher-order thinking tasks such as analysis, synthesis, and evaluation
(Bloom 1956; Wilke 2003) as they construct their own understanding of the information. In other
words, AL is an instructional method which engages students (Prince 2004); solicits active
participation on the part of the students (Olgun 2009); and integrates learning through
encouragement and feedback (Bernhard 2001).
The underpinnings of AL can be directly linked to constructivism. From the theoretical
perspective of teaching and learning, constructivism hypothesizes that knowledge is constructed
through learning experiences that are facilitated by the others (e.g. teachers, fellow students), in
social processes and relationships (Abdul-Haqq 1998; Vygotsky 1978; Wink & Putney 2002).
Learning is considered a product of ordering and synthesizing new information facilitated by the
interaction of educational materials, instructor, and peer social group (Vygotsky 1978). This is
precisely where EPMs would find their applications: elevating student engagement using social
interactions with peers, through authentic discussions regarding the impact of recent information
on the market prices. There is emerging evidence that when a prediction market is integrated into
traditional communication channels, it can reinforce students’ thinking in a wider range than
what is proposed by the instructor (O’Toole & Absalom 2003). This means that the structure of
Journal of Professional Issues in Engineering Education and Practice. Submitted June 21, 2011; accepted May 22, 2012;
posted ahead of print May 24, 2012. doi:10.1061/(ASCE)EI.1943-5541.0000127
Copyright 2012 by the American Society of Civil Engineers
J. Prof. Issues Eng. Educ. Pract.
Downloaded from ascelibrary.org by Texas A&M Univ College Station on 11/01/12. Copyright ASCE. For personal use only; all rights reserved.
Accepted Manuscript
Not Copyedited
5
prediction markets would allow students to engage in a learning experience using both channels
of cognition and corresponding actions analytical (i.e. using formal methods and processes),
and affective (i.e. using “gut feeling” and instincts) (Garvey and Buckley 2010). Hence,
prediction markets meet the conditions required by the Active Learning approach (Buckley et al
2011).
CASE STUDY
The educational prediction market was implemented in two engineering classes focusing on
project management: a) Project Management for Engineers, a multi-disciplinary class for civil,
industrial, and mechanical engineering undergraduates, and b) Civil Engineering Project
Management, a required junior-level class in the civil engineering department. The market
implementation was conducted in the Fall of 2010. The two courses were taught by Drs.
Damnjanovic and Reinschmidt.
The market questions were based on well-defined milestone points of construction activities for a
building project for the University’s veterinary school, while progress data were provided by the
actual construction contractor’s project team. Figure 1 shows a 3-D computer rendering of the
project and a site photo.
INSERT FIGURE 1
A total number of 122 student users participated in the market. Initially, all students were
allocated $5,000 to buy or sell stocks in two predefined project milestone “stocks” (“Structure
Top Out” and “Substantial Dry-in”) that were initially priced at $50 per share, reflecting the
initial likelihood of meeting the schedule milestone points of 50 percent. In other words, students
individually decide to buy (or sell) shares in a particular milestone event which they think will be
Journal of Professional Issues in Engineering Education and Practice. Submitted June 21, 2011; accepted May 22, 2012;
posted ahead of print May 24, 2012. doi:10.1061/(ASCE)EI.1943-5541.0000127
Copyright 2012 by the American Society of Civil Engineers
J. Prof. Issues Eng. Educ. Pract.
Downloaded from ascelibrary.org by Texas A&M Univ College Station on 11/01/12. Copyright ASCE. For personal use only; all rights reserved.
Accepted Manuscript
Not Copyedited
6
successful (or unsuccessful). Success in EPM would provide them with a profit” over their
original $5,000 at the end of the market period. Students were advised by the instructors that
their grades in the class could be affected by their participation and success in trading. To
provide the basis for making the trades, the contractor provided internal weekly reports and
schedule updates along with a series of construction site photos. This process gave students
experience with reading authentic weekly reports and schedule updates. Additionally, students
could also visit the site personally and directly observe the status with regard to these milestones.
The EPM used the Hanson model (Hanson 1999) to determine the prices and avoid liquidity
issues (i.e., when someone is willing to buy at a certain price, but no one is offering to sell at any
price). Furthermore, the Hanson model includes the ability to short-sell (i.e., to sell stock without
actually owning it at the time of transaction). Over the course of the semester, students made
over 2,500 trades (buy, sell, or sell short) responding to the contractor reports, weather forecasts,
and other information they found of value in determining whether the stocks are under-valued or
over-valued. Figures 3 and 4 show the price changes throughout the entire duration of the market
including the daily trading volumes (sells and buys) shown as bars at the bottom of the figures.
As can be seen from both figures, peaks in trading volumes tended to occur in intervals of one
week, when the contractors posted their reports and the images from the construction site became
available.
INSERT FIGURE 2
INSERT FIGURE 3
Chronology of Market Implementation.
Journal of Professional Issues in Engineering Education and Practice. Submitted June 21, 2011; accepted May 22, 2012;
posted ahead of print May 24, 2012. doi:10.1061/(ASCE)EI.1943-5541.0000127
Copyright 2012 by the American Society of Civil Engineers
J. Prof. Issues Eng. Educ. Pract.
Downloaded from ascelibrary.org by Texas A&M Univ College Station on 11/01/12. Copyright ASCE. For personal use only; all rights reserved.
Accepted Manuscript
Not Copyedited
7
In the first week of trading, the buyers outnumbered sellers resulting in price increases,
from initially $50 per share to around $80 by the end of the week. This effect can possibly be
attributed to the class presentation given by the construction contractor’s project manager on
September 20, in which he stressed the importance of meeting project milestones. Students’
comments such as the one below show the eagerness to buy the shares: “I think it might be time
to buy this question. NOW! This is the lowest it’s ever been so buy quickly.” In the middle of the
second week, on Sep. 28, the first weekly report and project schedule update were posted. The
new project schedule showed that the first milestone (i.e. Structure Top Out) would be completed
with a 4 day delay, on Oct. 26 rather than Oct 22; and the second one (i.e. Substantial Dry-in)
would be finished by Dec. 2 with a 6 day delay. These anticipated delays affected students’
confidence and some of them started to change their minds about the milestones’ completion
times. Furthermore, the summary daily activity reports indicated problems with steel fabrication.
As a result, in this period, the prices of both stocks fell significantly. The following two sample
comments illustrate students’ feelings: “Daily Report of 9/23 says that rebar for TOMO roof
was fabricated incorrectly…which in turn pushes back TOMO roof pour from this Wednesday to
this Thursday or Friday….not good sign overall.” The revised schedule published on Oct. 6
brought back the belief that the milestone points would be met. These new dates implied a
positive answer to both prediction questions, thus, students responded by buying shares at
discounted prices (in the range from $40 to $50), with anticipation that the final share price
would be $100 and they would cash in at profits from $60 to $50 per share. The comments
below illustrate this sentiment: “Current updates say all systems go so project is now on route to
success in reaching Oct 22! Actually says it will be done on October 20!” or this one: “You’ll
win tons of money. But after the previous report release its looking like the stock will go to 100. I
Journal of Professional Issues in Engineering Education and Practice. Submitted June 21, 2011; accepted May 22, 2012;
posted ahead of print May 24, 2012. doi:10.1061/(ASCE)EI.1943-5541.0000127
Copyright 2012 by the American Society of Civil Engineers
J. Prof. Issues Eng. Educ. Pract.
Downloaded from ascelibrary.org by Texas A&M Univ College Station on 11/01/12. Copyright ASCE. For personal use only; all rights reserved.
Accepted Manuscript
Not Copyedited
8
wouldn’t recommend shorting unless you personally plan on sabotaging the construction site.”
There were no schedule updates in the Oct. 19 schedule charts, so students were left to wonder
and analyze daily reports for clues. The following comments clearly present the students’
reasoning process: As per reports, steel erection (structure top out) scheduled to start on Oct.
7th with a crane and end on Oct. 22. According to the reports, the crane doesn’t show up until
Oct. 13th. The steel was erected using a fork lift from Oct. 7th to Oct. 13th. Hmm interesting?”
The revised schedule posted on Oct. 21 indicated “Structure Top Out” milestone with a
completion date of Oct. 27, five days later than in the initial report and the first prediction
requirement, and “Substantial Dry-in” milestone with a completion date of Dec. 3, more than 7
days later than that in the prediction question. Students saw this as sign of sure failure to reach
the milestone and continued to sell and short-sell, driving the price down to close to $0. “Game
Over. Sell while you can or just beg for a bail out, I mean its what all the cool kids are doing
today.”, or “Game Over. Might as well short sell before it hits zero. Check the schedule. On
Oct 22, “Structure Top Out” was closed and the share value set to $0. The reports posted on Nov.
9 and 17 did not make any changes to the falling prices for the second question. On Nov. 25 the
project manager informed the students that the second milestone point was not met as well.
Data Collection
A six-item Likert survey, with a 5 point rating scale, was developed to collect data on students’
learning experience with EPM. The survey required students to rate the value of the prediction
markets as a learning tool for a) engagement for learning, b) understanding realities of
construction, c) knowledge of probability, and d) advanced project management concepts.
Additionally, using the same 5 point scale, the students rated the three other major instructional
resources traditionally used in the course: a) homework assignments, b) lectures, c) textbook.
Journal of Professional Issues in Engineering Education and Practice. Submitted June 21, 2011; accepted May 22, 2012;
posted ahead of print May 24, 2012. doi:10.1061/(ASCE)EI.1943-5541.0000127
Copyright 2012 by the American Society of Civil Engineers
J. Prof. Issues Eng. Educ. Pract.
Downloaded from ascelibrary.org by Texas A&M Univ College Station on 11/01/12. Copyright ASCE. For personal use only; all rights reserved.
Accepted Manuscript
Not Copyedited
9
The ratings of mult iple learning tools on the same scale allo wed for relat ive comparisons
between learning activities. Finally, to collect qualitative data, students answered two open
ended questions about the use of EPM as a learning tool. Surveys were conducted during the
final class meeting for the semester. To reduce bias from social desirability, surveys were
conducted anonymously.
INSERT TABLE 1
Discussion
To what extent do prediction markets as a teaching tool enhance undergraduate students’
engagement in a construction/project management course? In summary, over 70% of students
agreed or strongly agreed that the prediction market was engaging. For comparison, slightly
more students, 74.1%, agreed or strongly agreed that class lecture/lecture notes captured their
interest. However, fewer students, 59.3% and 35.8% respectively, felt that homework
assignments and textbooks were engaging. Although lecture notes and textbooks are certainly
not perfect, they took years to develop with feedback from students and instructors; the
prediction market approach has had much less opportunity for development and refinement.
This may be one of the reasons why lecture notes were ranked to be more engaging than EPM.
In response to whether prediction markets evoked positive emotional responses (i.e., enjoyment)
approximately 63% agreed that this instructional exercise enacted a positive emotional response.
In comparison, slightly more students (66.7%) rated the lecture/lecture notes as eliciting a
positive response. Markedly fewer students rated the homework and textbook, 43.2% and
24.7% respectively, as creating positive emotions.
Journal of Professional Issues in Engineering Education and Practice. Submitted June 21, 2011; accepted May 22, 2012;
posted ahead of print May 24, 2012. doi:10.1061/(ASCE)EI.1943-5541.0000127
Copyright 2012 by the American Society of Civil Engineers
J. Prof. Issues Eng. Educ. Pract.
Downloaded from ascelibrary.org by Texas A&M Univ College Station on 11/01/12. Copyright ASCE. For personal use only; all rights reserved.
Accepted Manuscript
Not Copyedited
10
When analyzing the qualitative reports in response to the open-ended question, In what ways did
the prediction market facilitate your learning in this course?, numerous students cited that the
prediction market exercise helped promote interest in the course. These responses support the
Likert ratings, and also give insight for the rationale for the ratings. The reasons for their
increased interest differed among students. Some students linked their interest in the prediction
market to the project’s connection to real-world construction. For example it … allowed an
insight into the actual events at a construction project which to me was interesting. Other
students reported that it was interesting because it helped them to interact with the data, for
example: It helped to understand how to take data and make predictions …how most [people]
react to changes and the effect it has; I stayed interested this semester, not bored. Additionally,
students attributed interest to the interaction among students in the prediction process. For
example: It made the class interesting. People tried to convince each other to predict a certain
way.
In order to measure both positive and negative emotions (e.g., frustration) evoked by each of
these learning resources, we also included the following statement: This instructional resource
evoked a negative emotional response. We were interested if the competitive aspect of the
assignment would be stressful for certain students. However, the results indicate that the
majority of students did not experience negative emotions from the prediction market learning
experience: 22.2% strongly disagreed, 33.3% disagreed, 19.8% were neutral, 18.5% agreed and
only 6.2% strongly agreed with the statement that predictive markets raise negative feelings. In
other words, 24.7% experienced negative emotions through this learning experience and 75.3%
did not.
Journal of Professional Issues in Engineering Education and Practice. Submitted June 21, 2011; accepted May 22, 2012;
posted ahead of print May 24, 2012. doi:10.1061/(ASCE)EI.1943-5541.0000127
Copyright 2012 by the American Society of Civil Engineers
J. Prof. Issues Eng. Educ. Pract.
Downloaded from ascelibrary.org by Texas A&M Univ College Station on 11/01/12. Copyright ASCE. For personal use only; all rights reserved.
Accepted Manuscript
Not Copyedited
11
Again, the open-ended responses gave insight as to the quantitative ratings as to why some
students experienced negative emotions with this project. Multiple students reported confusion
with using the tool. One student reported that: it took him/her about a month to fully understand
how to use the tool and what everything meant. Other students recommended more instructor
clarification: I think a little guidance on what exactly was happening when we bought or sold
stock would be helpful. I didn’t really know what I was doing. It is important to note that the
students were provided with information about how to use the predictive market tool but
deliberately not provided with instruction in probability theory or applications, as one objective
was to observe how students used their best judgment in assessing the likelihood of events.
Additionally, some students were frustrated that the system could be “gamed”. For example, one
student reported It was easy to manipulate the system that required no expertise in the field of
scheduling. However, there is no conclusive evidence that manipulation was effective in
moving the markets, although it is true that there was very high variability in the wealth of
different students at the end of the simulation. If there was manipulation, it may be because
some students did not take the exercise seriously while other students took the exercise very
seriously. Those students who complained about manipulation may have been rationalizing their
lack of confidence in their own ability to succeed with the prediction markets. More research is
needed to resolve these questions about student involvement that were raised by the short
experiment reported here.
Finally, more than two-thirds of the students, or more precisely 69% agreed or strongly agreed
that the EPM helped them to connect to the real world. For context, slightly more students,
76.6%, agreed or strongly agreed that lecture/lecture notes helped them understand the realities
of construction practice. However, significantly fewer students, 54.4% and 53.1% respectively,
Journal of Professional Issues in Engineering Education and Practice. Submitted June 21, 2011; accepted May 22, 2012;
posted ahead of print May 24, 2012. doi:10.1061/(ASCE)EI.1943-5541.0000127
Copyright 2012 by the American Society of Civil Engineers
J. Prof. Issues Eng. Educ. Pract.
Downloaded from ascelibrary.org by Texas A&M Univ College Station on 11/01/12. Copyright ASCE. For personal use only; all rights reserved.
Accepted Manuscript
Not Copyedited
12
felt that homework assignments and textbooks helped them understand the realities of
construction. Students’ qualitative responses supported these ratings. Students mentioned issues
regarding application to the real world more often than any other category of response.
Representative responses included: The weekly reports were good insight into real documents
and real work in the field and how … it differs from homework and textbook examples.
Students noted that it helped to develop an understanding of how things [are] on a jobsite …
taught me what project managers need to plan for in a project. Students also reported that the
exercise gave them confidence because it helped them to familiarize themselves with project
progress reports and the complexity of scheduling in project management situations.
LESSONS LEARNED, LIMITATIONS, AND FUTURE RESEARCH
The findings of this study offer evidence that teaching undergraduate students through an active
learning method. More specifically, implementing prediction markets can promote engagement
and provide a better link between the classroom and what actually occurs on the construction
site. Students were afforded the learning opportunity to use their best judgment, or take relevant
material from the classroom curriculum and apply it to make trades.
Our feedback leads us to several recommendations. To have an effective EPM in construction
management applications, it is essential to: a) select a project, preferably a site that students have
some visual access to, or can walk by, b) carefully select milestone points that are well scoped
and visible to the participants, so there is no ambiguity in interpretation of the outcome, and c)
provide real and unedited data that students can use to make educated trade decisions.
One of the major concerns raised was gaming the system”. For example, students posting
misleading comments to influence trading decisions of others, so that they can profit from short -
Journal of Professional Issues in Engineering Education and Practice. Submitted June 21, 2011; accepted May 22, 2012;
posted ahead of print May 24, 2012. doi:10.1061/(ASCE)EI.1943-5541.0000127
Copyright 2012 by the American Society of Civil Engineers
J. Prof. Issues Eng. Educ. Pract.
Downloaded from ascelibrary.org by Texas A&M Univ College Station on 11/01/12. Copyright ASCE. For personal use only; all rights reserved.
Accepted Manuscript
Not Copyedited
13
term price changes. The point is that the actions by some students in manipulating or “gaming”
the system appear to violate some unstated ethical principle that the game should be fair and
equitable to all users. If it is not, then the other students may feel that playing the market might
not be a good use of their time and effort, thus dividing the students into two categories: those
who played the game straight (i.e., treated the market seriously and focused on the prediction that
the milestone event would be on time), and those who tried to manipulate the others by preying
on the fears of the other students and persuading other players to take actions contrary to their
own interests (e.g., by advising other students to buy when the manipulator was sure that the
milestone event would not be on time and the market would fall). Although it was clear to all
that the prediction market was just a game (real money did not change hands) more research
needs to be done concerning the reactions of students to the ethical issues raised by this
manipulation and /or how such manipulation should be addressed. In summary, however, the
reactions by the manipulators, who spent considerable time on preparing their strategies, and by
those straight students who resented these manipulations indicate the high level of engagement in
the prediction markets by both categories of students.
Several limitations of this study focus on the survey instrument, and the single form of data
collection. First, the survey was a newly designed instrument, and had not been previously
tested. In addition, the data collection process for the study was based on a self-report measure.
Answers provided by participants may be influenced by the desire to provide responses which
are socially acceptable. For example, participants may rate their understanding of the realities of
construction practices as agree or strongly agree, because they were successful in utilizing the
prediction market. Third, the study only used one form of data collection. The survey instrument
was only administered to the participants at the end of the treatment with the implementation of
Journal of Professional Issues in Engineering Education and Practice. Submitted June 21, 2011; accepted May 22, 2012;
posted ahead of print May 24, 2012. doi:10.1061/(ASCE)EI.1943-5541.0000127
Copyright 2012 by the American Society of Civil Engineers
J. Prof. Issues Eng. Educ. Pract.
Downloaded from ascelibrary.org by Texas A&M Univ College Station on 11/01/12. Copyright ASCE. For personal use only; all rights reserved.
Accepted Manuscript
Not Copyedited
14
the prediction markets. The use of a comparison group was not employed which does not allow
us to consider causality of learning.
To gain further insights in ability of predication markets to promote students’ learning of project
risks, we plan to extend the study effort to include full experimental design with control and
treatment groups. More specifically, we plan to look for the effects of both endogenous and
exogenous factors on learning outcomes, as well as investigate modalities of integrating
prediction markets into class material on project risk management.
SUMMARY
This paper presents an investigation of the effects of using prediction markets in teaching
engineering project risks. We focused on two important aspects of teaching that are critical to the
students ability to study abstract concepts such as risks: engagement and realism. To analyze
how prediction markets affect engagement and realism in learning environment we have
formulated four research questions, implemented the market, and collected data. The study
results show that the use of prediction markets were particularly valuable for helping students
make connections to real world applications and to help promote interest and enthusiasm which
are integral factors in long-term learning.
REFERENCES
Abdul-Haqq, I. (1998). Constructivism in teacher education: considerations for those who would
link practice to theory. ERIC Clearinghouse on Teaching and Teacher Education.
Arrow, K. (2008). The promise of prediction markets, Science, 320, 877-878
Journal of Professional Issues in Engineering Education and Practice. Submitted June 21, 2011; accepted May 22, 2012;
posted ahead of print May 24, 2012. doi:10.1061/(ASCE)EI.1943-5541.0000127
Copyright 2012 by the American Society of Civil Engineers
J. Prof. Issues Eng. Educ. Pract.
Downloaded from ascelibrary.org by Texas A&M Univ College Station on 11/01/12. Copyright ASCE. For personal use only; all rights reserved.
Accepted Manuscript
Not Copyedited
15
Bernhard J. (2001). Do active engagement curricula give long-lived conceptual understanding?
in R. Pinto & S. Surinach (Eds.) Physics Teacher Education Beyond 2000 (pp. 749-752).
Paris, Elsevier.
Berg, J. E., Nelson, F. D., & Rietz, T. A. (2008). Prediction market accuracy in the long run,”
International Journal of Forecasting, 24 (2), 285-300.
Berg, J. E., Nelson, F., & Rietz, T. A. (2003). Accuracy and Forecast Standard Error of
Prediction, Working Paper, Tippie College of Business, University of Iowa.
Bloom, B. (1956). (Ed.). Taxonomy of Educational Objectives, the classification of educational
goals Handbook I: Cognitive Domain. New York: McKay.
Buckley, P., Garvey, J., & McGrath, F. (2011). A case study on using prediction markets as a
rich environment for active learning,” Computers & Education, 56, 418428.
Garvey, J., & Buckley, P. (2010). Teaching the Concept of Risk: Blended Learning Using a
Custom-Built Prediction Market,” Journal of Teaching in International Business, 21,
346357.
Hanson R. D. (1999). Prediction markets,” IEEE Intelligent System 14, 16-19
Olgun, O. S. (2009). Engaging elementary preservice teachers with active learning teaching
methodologies,” The Teacher Educator, 44(2), 113-125.
O’Toole, J. M., & Absalom, D. J. (2003). The impact of blended learning on student outcomes:
Is there room on the horse for two? Journal of Education Media, 28 (2-3), 179190.
Journal of Professional Issues in Engineering Education and Practice. Submitted June 21, 2011; accepted May 22, 2012;
posted ahead of print May 24, 2012. doi:10.1061/(ASCE)EI.1943-5541.0000127
Copyright 2012 by the American Society of Civil Engineers
J. Prof. Issues Eng. Educ. Pract.
Downloaded from ascelibrary.org by Texas A&M Univ College Station on 11/01/12. Copyright ASCE. For personal use only; all rights reserved.
Accepted Manuscript
Not Copyedited
16
Prince, M. (2004). Does active learning work? A review of the research. Journal of
Engineering Education, 93(3), 1-9.
Tziralis, G., & Tatsiopoulos, I. (2007). Prediction markets: An extended literature. The Journal
of Prediction Markets, Vol. 1, 7591.
Vygotsky, L.S. (1978). Mind in society: The development of higher psychological functions.
Cambridge, MA: Harvard University Press.
Wilke, R. R. (2003). The effect of active learning on student characteristics in a human
physiology course for nonmajors,” Advances in Physiology Education, 27(4), 207-223.
Wink, J., & Putney, L. (2002). A Vision of Vygotsky. Boston, MA: Allyn and Bacon.
Wolfers, J., & Zitzewitz, E. (2004). Prediction Markets,” Journal of Economic Perspectives, 18
(2), 107-126.
Journal of Professional Issues in Engineering Education and Practice. Submitted June 21, 2011; accepted May 22, 2012;
posted ahead of print May 24, 2012. doi:10.1061/(ASCE)EI.1943-5541.0000127
Copyright 2012 by the American Society of Civil Engineers
J. Prof. Issues Eng. Educ. Pract.
Downloaded from ascelibrary.org by Texas A&M Univ College Station on 11/01/12. Copyright ASCE. For personal use only; all rights reserved.
Accepted Manuscript
Not Copyedited
FIGURE CAPTION LIST
Figure 1. 3D Model of TAMU Vet Imaging and Cancer Treatment Center and the Site Photo
Figure 2. Price Changes for “Structure Top Out” Milestone Point
Figure 3. Price Changes for “Substantial Dry-in” Milestone Point
)LJXUH&DSWLRQOLVW
Journal of Professional Issues in Engineering Education and Practice. Submitted June 21, 2011; accepted May 22, 2012;
posted ahead of print May 24, 2012. doi:10.1061/(ASCE)EI.1943-5541.0000127
Copyright 2012 by the American Society of Civil Engineers
J. Prof. Issues Eng. Educ. Pract.
Downloaded from ascelibrary.org by Texas A&M Univ College Station on 11/01/12. Copyright ASCE. For personal use only; all rights reserved.
Accepted Manuscript
Not Copyedited
Journal of Professional Issues in Engineering Education and Practice. Submitted June 21, 2011; accepted May 22, 2012;
posted ahead of print May 24, 2012. doi:10.1061/(ASCE)EI.1943-5541.0000127
Copyright 2012 by the American Society of Civil Engineers
J. Prof. Issues Eng. Educ. Pract.
Downloaded from ascelibrary.org by Texas A&M Univ College Station on 11/01/12. Copyright ASCE. For personal use only; all rights reserved.
)LJXUH
Accepted Manuscript
Not Copyedited
Journal of Professional Issues in Engineering Education and Practice. Submitted June 21, 2011; accepted May 22, 2012;
posted ahead of print May 24, 2012. doi:10.1061/(ASCE)EI.1943-5541.0000127
Copyright 2012 by the American Society of Civil Engineers
J. Prof. Issues Eng. Educ. Pract.
Downloaded from ascelibrary.org by Texas A&M Univ College Station on 11/01/12. Copyright ASCE. For personal use only; all rights reserved.
)LJXUHQHZ
Accepted Manuscript
Not Copyedited
Journal of Professional Issues in Engineering Education and Practice. Submitted June 21, 2011; accepted May 22, 2012;
posted ahead of print May 24, 2012. doi:10.1061/(ASCE)EI.1943-5541.0000127
Copyright 2012 by the American Society of Civil Engineers
J. Prof. Issues Eng. Educ. Pract.
Downloaded from ascelibrary.org by Texas A&M Univ College Station on 11/01/12. Copyright ASCE. For personal use only; all rights reserved.
Table 1: Students’ Survey Results Related to Effectiveness of Prediction Markets
Strongly Agree
Agree
Neutral
Disagree
Strongly Disagree
28.4
42
14.8
9.9
4.9
50.6
12.3
21
8.6
7.4
6.2
18.5
19.8
33.3
22.2
39.5
29.6
16
8.6
6.2
7DEOH
Accepted Manuscript
Not Copyedited
Journal of Professional Issues in Engineering Education and Practice. Submitted June 21, 2011; accepted May 22, 2012;
posted ahead of print May 24, 2012. doi:10.1061/(ASCE)EI.1943-5541.0000127
Copyright 2012 by the American Society of Civil Engineers
J. Prof. Issues Eng. Educ. Pract.
Downloaded from ascelibrary.org by Texas A&M Univ College Station on 11/01/12. Copyright ASCE. For personal use only; all rights reserved.
... Dado que o ensino de estudantes através de um método de aprendizagem ativa é eficaz(DAMNJANOVIC et al., 2012), faz-se fundamental incorporar continuamente atividades de aprendizagem ativa nos cursos, de forma que seus benefícios precisam ser compartilhados com os alunos para que eles desenvolvam uma apreciação desse método de ensino e o valor de adquirir habilidades de aprendizagem ao longo da vida (RIVKIN; GIM, 2013). Deste modo, na Tabela 4 apresenta-se as áreas em que as metodologias ativas foram empregadas na literatura.A educação médica está enfrentando uma crise quanto ao método de ensino utilizado nas salas de aula, de modo que métodos alternativos que não exigem altas taxas de comprometimento entre professores e alunos estão sendo buscados para reformular os currículos destes cursos(FATMI et al., 2013).Os métodos tradicionais para o treinamento de engenheiros estão sendo questionados, dado os novos conhecimentos e habilidades exigidos pelo mercado de trabalho (REYES; GÁLVEZ; 2010), onde o ensino efetivo de conceitos de engenharia depende tanto de aulas cuidadosamente elaboradas, que atendem a resultados específicos de aprendizado, como de atividades em sala que envolvam ativamente os estudantes na aprendizagem(DAMNJANOVIC et al., 2012).De acordo com Stewart et al. (2011) a base de conhecimento relacionado ao campo da saúde continua a crescer, de modo que torna-se impossível aumentar proporcionalmente o tempo ou a duração da aula presencial, de modo que os docentes deverão aceitar os desafios que a nova geração de educação farmacêutica apresenta, adotando, para tanto, métodos mais ativos de aprendizagem.Através das metodologias ativas, os alunos do ensino básico interagem e criam conteúdos próprios relacionados a áreas curriculares com diversas vantagens, tendo motivação, comprometimento, diversão e entusiasmo, mostrando melhorias relacionadas ao aprendizado (LÓPEZ; GONZÁLEZ; CANO, 2016). ...
... Dado que o ensino de estudantes através de um método de aprendizagem ativa é eficaz(DAMNJANOVIC et al., 2012), faz-se fundamental incorporar continuamente atividades de aprendizagem ativa nos cursos, de forma que seus benefícios precisam ser compartilhados com os alunos para que eles desenvolvam uma apreciação desse método de ensino e o valor de adquirir habilidades de aprendizagem ao longo da vida (RIVKIN; GIM, 2013). Deste modo, na Tabela 4 apresenta-se as áreas em que as metodologias ativas foram empregadas na literatura.A educação médica está enfrentando uma crise quanto ao método de ensino utilizado nas salas de aula, de modo que métodos alternativos que não exigem altas taxas de comprometimento entre professores e alunos estão sendo buscados para reformular os currículos destes cursos(FATMI et al., 2013).Os métodos tradicionais para o treinamento de engenheiros estão sendo questionados, dado os novos conhecimentos e habilidades exigidos pelo mercado de trabalho (REYES; GÁLVEZ; 2010), onde o ensino efetivo de conceitos de engenharia depende tanto de aulas cuidadosamente elaboradas, que atendem a resultados específicos de aprendizado, como de atividades em sala que envolvam ativamente os estudantes na aprendizagem(DAMNJANOVIC et al., 2012).De acordo com Stewart et al. (2011) a base de conhecimento relacionado ao campo da saúde continua a crescer, de modo que torna-se impossível aumentar proporcionalmente o tempo ou a duração da aula presencial, de modo que os docentes deverão aceitar os desafios que a nova geração de educação farmacêutica apresenta, adotando, para tanto, métodos mais ativos de aprendizagem.Através das metodologias ativas, os alunos do ensino básico interagem e criam conteúdos próprios relacionados a áreas curriculares com diversas vantagens, tendo motivação, comprometimento, diversão e entusiasmo, mostrando melhorias relacionadas ao aprendizado (LÓPEZ; GONZÁLEZ; CANO, 2016). ...
Article
Full-text available
O aprendizado ativo constitui como um novo paradigma na educação de qualidade, colaborativa, envolvente e motivadora, corroborando no ensino-aprendizagem, dado que a educação não pode mais ser considerada uma prática simples. Diante dessa perspectiva, o presente trabalho tem por objetivo identificar como as metodologias ativas estão sendo aplicadas nas instituições de ensino atuais. Para tanto, foi realizada uma revisão sistemática de literatura sobre o conceito de métodos de ensino ativo nos últimos 10 anos. As descobertas oferecem recomendações teóricas, dado que proporciona um panorama acerca do tema, e práticas, uma vez que apresenta um primeiro caminho para os profissionais utilizarem esses métodos, como características, metodologias ativas existentes, disciplinas aplicáveis, entre outros.
... The SD model was able to rearrange the projects in the given program to come up with the better sequencing of the projects in regard to saving more money at a given time. Damnjanovic et al. (2013) developed a model of predicting project future and its milestones using prediction market with Hanson calculation method. By the use of that proposed tool, a project manager and his/her team can have better insight of the project, helping them in wiser rescheduling of the project plan. ...
... Market (Damnjanovic, Faghihi, Scott, McTigue, & Reinschmidt, 2013), can be used alongside the extended version of this proposed methodology. ...
Thesis
Full-text available
Construction project scheduling is one of the most important tools for project managers in the Architecture, Engineering, and Construction (AEC) industry. The Construction schedules allow project managers to track and manage the time, cost, and quality (i.e. Project Management Triangle) of projects. Developing project schedules is almost always troublesome, since it is heavily dependent on project planners’ knowledge of work packages, on-the-job-experience, planning capability, and oversight. Having a thorough understanding of the project geometries and their internal interacting stability relations plays a significant role in generating practical construction sequencing. On the other hand, the new concept of embedding all the project information into a three-dimensional (3D) representation of a project (a.k.a. Building Information Model or BIM) has recently drawn the attention of the construction industry. In this dissertation, the author demonstrates how to develop and extend the usage of the Genetic Algorithm (GA) not only to generate construction schedules, but to optimize the outcome for different objectives (i.e. cost, time, and job-site movements). The basis for the GA calculations is the embedded data available in BIM of the project that should be provided as an input to the algorithm. By reading through the geometry information in the 3D model and receiving more specific information about the project and its resources from the user, the algorithm generates different construction schedules. The output Pareto Frontier graphs, 4D animations, and schedule wellness scores will help the user to find the most suitable construction schedule for the given project.
... Academic governance and product design should be in relation to the requirements to the educational market (Adina & Liviu, 2013) as it remains a well acknowledged fact that education predicts markets for employment (Damnjanovic et al.,2013). The students patterns of use as per ones academic major classifies an academic institution (Bahr, 2013a) where students have limited access to institutions when it comes to selecting prestigious top institutions and choice of academic major that relied heavily on institution feasibility (Tavares, 2013). ...
Article
Full-text available
Higher education is known for multitude of institutions who are on a rampage to provide for best of best education to every student. A student is often left with dilemmas where all institute provide for similar courses of same duration. The choice of one's institute is often left with multiple parameters though often striding the take away with the availability of ones choice of academic major. Nevertheless, over the years of being one at campus, the student often relinquishes the internal and external environment of institution to be a cherished with said parameters that serve as benchmarks for future generations to opt as one.
... There are several studies dealing with the use of PMs in the area of education. For example Ellis and Sami [13] in political science courses, or Damnjanovic et al. [14] in project management courses. Buckley, Garvey, and McGrath [15] use the PM principle in the social science and economics courses to develop the orientation and decision making processes. ...
Conference Paper
Prediction Market serves as an alternative tool mainly applied to gather the information widespread among the numerous experts. This tool can be used as a supplementary teaching aid in the financial engineering courses. The outcomes of selected markets give also the useful continuous feedback to the teachers. The contribution is the focus on motivational and incentive system. The prediction market inflation is introduced as motivation tool. The participants' activity is analyzed by the influence of inflation engagement. Two groups of market participants are compared with respect to participants' activity and inflation administration in the experiment. The comparison is maintained on the same market in the same conditions for all participants. The implemented system of signals allows to apply inflation only to the selected group and in the selected periods during the experiments. Finally, the increased number of the active shares on the counts of the participants' activity is considered.
... An extension of the same research investigated the combined interaction of human behavior modification and hardware improvement in monetary savings as well as timing and sequencing of the projects [16]. Damnjanovic et al. [10] developed a model of predicting a project's future and its milestones using prediction markets with the Hanson calculation method. By the use of that proposed tool, a project manager and his/her team can gain better insight into the project, helping them reschedule the project plan effectively. ...
Article
Full-text available
Automating the development of construction schedules has been an interesting topic for researchers around the world for almost three decades. Researchers have approached solving scheduling problems with different tools and techniques. Whenever a new artificial intelligence or optimization tool has been introduced, researchers in the construction field have tried to use it to find the answer to one of their key problems—the “better” construction schedule. Each researcher defines this “better” slightly different. This article reviews the research on automation in construction scheduling from 1985 to 2014. It also covers the topic using different approaches, including case-based reasoning, knowledge-based approaches, model-based approaches, genetic algorithms, expert systems, neural networks, and other methods. The synthesis of the results highlights the share of the aforementioned methods in tackling the scheduling challenge, with genetic algorithms shown to be the most dominant approach. Although the synthesis reveals the high applicability of genetic algorithms to the different aspects of managing a project, including schedule, cost, and quality, it exposed a more limited project management application for the other methods.
... There is even evidence of studies dealing with using PMs in teaching, for example Cali Mortenson and Rahul (2012) in teaching political science courses, or Damnjanovic et al. (2013) in teaching project management. Buckley, Garvey, and McGrath (2011) use the PM principle in teaching the subjects from the field of social science and economics to develop the orientation and decision making processes in a wide range of issues. ...
Article
Full-text available
Electronic virtual markets can serve as an alternative tool for collecting information that is spread among numerous experts. This is the principal market functionality from the operators’ point of view. On the other hand it is profits that are the main interest of the market participants. What they expect from the market is liquidity as high as possible and the opportunity for unrestricted trading. Both the operator and the electronic market participant can be considered consumers of this particular market with reference to the requirements for the accuracy of its outputs but also for the market liquidity. Both the above mentioned groups of consumers (the operators and the participants themselves) expect protection of their specific consumer rights, i.e. securing the two above mentioned functionalities of the market. These functionalities of the electronic market are, however, influenced by many factors, among others by participants’ activity. The article deals with the motivation tools that may improve the quality of the prediction market. In the prediction electronic virtual market there may be situations in which the commonly used tools for increasing business activities described in the published literature are not significantly effective. For such situations we suggest a new type of motivation incentive consisting in penalizing the individual market participants whose funds are not placed in the market. The functionality of the proposed motivation incentive is presented on the example of the existing data gained from the electronic virtual prediction market which is actively operated.
... After future extensions of the current work, the outcome can be tested to see the benefits of this methodology of automatic project scheduling from the project BIM in an educational environment. To do this evaluation, the previously proven tool of predicting future of the projects in educational environment, called Project Management Prediction Market (Damnjanovic, Faghihi, Scott, McTigue, & Reinschmidt, 2013), can be used alongside the extended version of this proposed methodology. ...
Conference Paper
Prediction Market serves as an alternative tool mainly applied to gather the information widespread among the numerous experts. This tool can be used as a supplementary teaching aid in the financial engineering courses. The outcomes of selected markets give also the useful continuous feedback to the teachers. The contribution is the focus on motivational and incentive system. The prediction market inflation is introduced as motivation tool. The participants’ activity is analyzed by the influence of inflation engagement. Two groups of market participants are compared with respect to participants’ activity and inflation administration in the experiment. The comparison is maintained on the same market in the same conditions for all participants. The implemented system of signals allows to apply inflation only to the selected group and in the selected periods during the experiments. Finally, the increased number of the active shares on the counts of the participants’ activity is considered.
Article
Full-text available
In recent years, Prediction Markets gained growing interest as a forecasting tool among researchers as well as practitioners, which resulted in an increasing number of publications. In order to track the latest development of research, comprising the extent and focus of research, this article provides a comprehensive review and classification of the literature related to the topic of Prediction Markets. Overall, 316 relevant articles, published in the timeframe from 2007 through 2013, were identified and assigned to a herein presented classification scheme, differentiating between descriptive works, articles of theoretical nature, application-oriented studies and articles dealing with the topic of law and policy. The analysis of the research results reveals that more than half of the literature pool deals with the application and actual function tests of Prediction Markets. The results are further compared to two previous works published by Zhao, Wagner and Chen (2008) and Tziralis and Tatsiopoulos (2007a). The article concludes with an extended bibliography section and may therefore serve as a guidance and basis for further research.
Article
Full-text available
Any survey of research about Information and Communication Technologies (ICT) in learning reveals a contested field. There is recognition of the enabling role of technology amidst enthusiastic calls for its widespread adoption, and there are sceptical responses to its implementation. However, little research exists regarding the impact of ICT on the achievement of student outcomes in specific undergraduate courses, particularly how student utilisation of varying modes within blended provision relates to their achievement of course outcomes.It was the purpose of this comprehensive study involving 72 final-year undergraduate teacher education students to understand the relationship of access mode within blended provision to student attitude and outcome in a specific situation. Findings reveal that ICT access formats by themselves are of limited benefit in achieving course outcomes. Indeed, in some instances, ICT modes can be seen to negatively affect student performance due to some misplaced confidence in the media that provides the material. The challenge then becomes one of designating the inter-acting roles of varying access modes in order to try to maximise outcomes, and this paper offers several possible strategies.The implications of this study are significant because of the failure of designated modes of access to achieve the expected outcomes in a senior undergraduate course. The study also provides some insight into the complexities of the blending process in attempting to incorporate new technology into current teaching situations, and in trying to identify practical directions to take in advancing the process of technological teaching.
Article
There has been much research into the role of technology in promoting student engagement and learning activity in third-level education. This article documents an innovative application of technology in a large, undergraduate business class in risk management. The students' learning outcomes are reinforced by activity in a custom-designed prediction market. The content of lectures are closely aligned to the student objectives within the prediction market, thus allowing the application of risk management practice while building knowledge through traditional delivery methods.
Article
The main purpose of this study was to investigate the effects of active learning on preservice teachers' dignity, energy, self-management, community, and awareness (DESCA) abilities, attitudes toward teaching, and attitudes toward science. Third year preservice teachers (n = 77) from two different classes were involved in the study. One intact class was assigned as the experimental group, whereas the other intact class was assigned as the comparison group. The comparison group students received the instruction by traditional teaching, and the experimental group received instruction through an active learning paradigm. DESCA abilities and attitudes were measured before and after instruction. Results revealed that there was a significant difference favoring the active learning instruction on preservice teachers' DESCA scores; however, there was no significant difference on preservice teachers' attitudes toward teaching and science.
Article
“Prediction markets” are designed specifically to forecast events such as elections. Though election prediction markets have been being conducted for almost twenty years, to date nearly all of the evidence on efficiency compares election eve forecasts with final pre-election polls and actual outcomes. Here, we present evidence that prediction markets outperform polls for longer horizons. We gather national polls for the 1988 through 2004 U.S. Presidential elections and ask whether either the poll or a contemporaneous Iowa Electronic Markets vote-share market prediction is closer to the eventual outcome for the two-major-party vote split. We compare market predictions to 964 polls over the five Presidential elections since 1988. The market is closer to the eventual outcome 74% of the time. Further, the market significantly outperforms the polls in every election when forecasting more than 100 days in advance.
Article
In this paper, prediction markets are presented as an innovative pedagogical tool which can be used to create a Rich Environment for Active Learning (REAL). Prediction markets are designed to make forecasts about specific future events by using a market mechanism to aggregate the information held by a large group of traders about that event into a single value. Prediction markets can be used to create decision scenarios which are linked to real-world events. The advantages of this approach in the cognitive and affective domains of learning are examined. The unique ability of prediction markets to enable active learning in large group teaching environments is explored. Building on this theoretical work, a detailed case study is presented describing how a prediction market can be deployed as a pedagogical tool in practice. Empirical evidence is presented exploring the effect prediction market participation has on learners in the cognitive domain.
Chapter
Soviet psychologists' views of the relationship between psychology and Pavlovian psychophysiology (or the study of higher nervous activity, as it is referred to in the Soviet literature) has long been a matter of curiosity and concern in the United States. Not accidentally, it has also been a matter of concern and dispute within the USSR. The following is an excerpt from a work by one of the Soviet Union's most seminal psychological theorists on this issue. Written in the late 1920s, this essay remains a classic statement of Soviet psychology's commitment to both a historical, materialistic science of the mind and the study of the unique characteristics of human psychological processes.
Article
This study examines the evidence for the effectiveness of active learning. It defines the common forms of active learning most relevant for engineering faculty and critically examines the core element of each method. It is found that there is broad but uneven support for the core elements of active, collaborative, cooperative and problem-based learning.