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The Impact of Learning Technologies on Higher Education

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Title: The Impact of Learning Technologies on Higher Education
extracted text: The Impact of Learning Technologies
on Higher Education
CHRISTOPHER S. PENTONEY, DIANE F. HALPERN, and HEATHER A. BUTLER

Abstract
Demand for higher education has created a need for learning technologies that can
accommodate the individualized needs of an increasing number of students. Thinking, learning, and memory have been studied extensively in their own right, but additional research on these topics in conjunction with advanced learning technologies
is needed. Developers of computerized tutoring systems, massive online courses,
and educational games will benefit from forward-thinking studies. Limitations are
constantly being lifted, and research must increase in pace to ensure the integrity of
upcoming learning technologies.

HOW ADVANCED LEARNING TECHNOLOGIES
ARE REINVENTING HIGHER EDUCATION
If all of the hype about reinventing, redefining, revolutionizing, and reinvigorating education is to be believed, we can expect fundamental changes in how
we teach and learn in the near future. Advanced learning technologies are the
engine fueling change. Automated assessments, customized feedback, and
gigantic online classrooms have already become reality to accommodate the
growing demand for flexible learning environments and a higher education
system that is more efficient, affordable, and accessible. A broad array of tools
for improving the way we “do” education are being developed as advances
in education and technology continuously shape each other.
As with any rapidly changing field, it can be difficult to discern the difference between substance and hype. The best of the advanced learning technologies are “created by designers who have a substantial theoretical and
empirical understanding of learners, learning, and the targeted subject matter” (Aleven, Beal, & Graesser, 2013, p. 929). These new technologies offer the
promise of achieving the gold standard for learning—creating deep knowledge that persists over time and transfers to new situations. The field is still in
Emerging Trends in the Social and Behavioral Sciences. Edited by Robert Scott and Stephen Kosslyn.
© 2015 John Wiley & Sons, Inc. ISBN 978-1-118-90077-2.

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its infancy, or perhaps toddlerhood, and thus currently offers more promise
of future development than evidence.
There is a great variety of advanced learning technologies—automated
assessment, personalized instruction, distance learning via proprietary
platforms, massive online open courses, intelligent tutoring systems (ITSs),
serious games (described later), and content-related activities such as
simulations and interactive reading, to name a few. It is very challenging to
design these emerging technologies, and venture capitalists are beginning
to pour resources into their development with the hope of striking it rich.
The excitement around the possibility of creating new and better ways of
learning is a welcome change to the status quo. There are numerous research
and practical issues to consider, and the hype surrounding new capabilities
should be grounded in realistic expectations.
The basic model of education has remained unchanged since the time
of Christ (and much earlier), who is often depicted talking to a group of
people to instruct them in the ways of religion. Similarly, teachers stand
before a group of students telling them what they need to know, stopping
occasionally for questions and discussions, but most of the time the communication is from teacher to learner. We can now envision better ways.
Rather than using a traditional classroom format, computers have the ability
to adapt to hordes of individual students located around the planet. As
access to the Internet becomes closer to a utility than a privilege for many
people, knowledge becomes more readily available. It was not long ago
when much of the world’s accumulated knowledge resided on the shelves
of elite libraries where only a privileged few had access. Advanced learning
technologies have democratized access to knowledge, which is a huge
sociological/political change that disseminates the power of knowledge
more widely than ever before in history. With the speed that progress is
being made, there is a need to keep the new technologies grounded in what
we know about how people learn, as well as a need for high-quality research
in the expanding field of technology-mediated education.
OVERVIEW OF BASIC LEARNING PRINCIPLES
Learning technologies should be informed by research that has identified
best practices in instruction. Ironically, the so-called “curse of knowledge”
can lead even innovative and motivated educational designers astray. This
phrase refers to the commonly experienced phenomenon in which experts
design systems that can only be used by other experts because they assume
that a theory or skill that is well known to them will be easily grasped by
novices. Rae-Dupree (2007, para. 7) describes the curse of knowledge this
way, “It’s why engineers design products ultimately useful only to other

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engineers. It’s why managers have trouble convincing the rank and file to
adopt new processes. And it’s why the advertising world struggles to convey
commercial messages to consumers.” We list here a few of the most salient
learning principles that need to be considered when designing new learning
technologies. An exhaustive list is beyond the scope of this essay, so we have
chosen to include those that we believe offer the greatest benefits.
PRESENT INFORMATION IN A WAY THAT REDUCES COGNITIVE LOAD
One of the advantages of learning technologies is that they are able to present
information to students in a variety of formats. For example, interactive
e-books are capable of containing embedded videos, audio recordings,
games, quizzes, interactive diagrams, and links to supplemental reading.
Although there are undeniable benefits to the use of these technologies,
the way in which information is presented to students affects their mastery
of that knowledge. Associated ideas should be presented according to
guidelines that facilitate connections in a meaningful way. Research on
contiguity effects shows that when text and images are related to each other
they should be placed near each other, in both space and in time (Mayer,
2009). For example, when learners have to search for mathematical diagrams
associated with a formula, the demands on working memory, known as
cognitive load, are increased and learning suffers (Ginns, 2006). The idea of
placing related pieces of information in close proximity to each other seems
like an obvious concept, but it can be easily overlooked.
Optimal learning materials involve more than visual displays. Information
is better remembered when it is delivered in multiple modes, such as
through auditory and visual methods combined (Mayer, 2009; Moreno &
Valdez, 2005), and in multiple formats such as graphs, animations, text, and
their combinations. Using varied modes of presentation enables more routes
for retrieval of that information later on, and the ability to manipulate the
various representations provides a level of learner engagement that would
not be possible without the new technologies that support it. For example,
when learners can physically manipulate kinematic graphs, they have a
better understanding of the underlying principles than those who watched
others manipulate the graphs (Anastopoulou, Sharples, & Baber, 2011).
Management of cognitive load is vital when considering how to present
information to learners.
DIVIDE INFORMATION INTO MANAGEABLE UNITS
The contiguity principle is not the only way to reduce the cognitive load of
learners. All pedagogical designs need to incorporate the fact that humans

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have a finite amount of working memory. Mayer and Moreno (2003) suggest that large amounts of new information be presented in discrete units
to reduce cognitive load, rather than all together at the same time. Learners
need to be allowed to process subunits of information before making connections between them. Furthermore, providing too much material at one
time or presenting irrelevant information can be harmful to learning. Certain information and images might be visually appealing, but if they are not
directly related to the topic being studied, they should be excluded (Kalyuga,
Chandler, & Sweller, 1999; Mayer, 2009).
Theorists usually differentiate cognitive load into three components: intrinsic (necessary for learning), extraneous (irrelevant to learning), and germane
(related to schema construction) (Sweller, van Merrienboer, & Paas, 1998).
For example, if a learner is having difficulty using educational software, the
additional strain associated with the operation of the software is irrelevant
to the material or skills being learned. A well-designed program will have
a low level of extraneous cognitive load, which means that the operation of
the program (e.g., key strokes needed to manipulate graphs or the way questions are worded) is intuitive. It will also have an optimal level of germane
load—the integration of information to-be-learned is neither too easy nor too
difficult for the learner. These three distinctions have been supported by confirmatory factor analysis (Leppink, Paas, Van der Vleuten, Van Gog, & Van
Merrienboer, 2013). It is important that the advanced learning technology not
be so complicated to operate that it interferes with learning.
BOOST LEARNING WITH REPEATED RETRIEVAL
The best way to make learning “stick” is with practice that involves repeated
retrieval from memory (Glass, 2009; Little, Bjork, Bjork, & Angello, 2012).
The basic idea underlying the superiority of repeated retrieval of information
from memory is that each item in memory has a probability associated with
its likelihood of being recalled. With repeated retrieval, the memory trace is
strengthened and information in memory becomes more likely to be recalled.
This principle has been dubbed the “testing effect” because retrieval usually
occurs in the context of a test. Broadly, findings from research on testing support giving students multiple, frequent examinations (Roediger & Karpicke,
2006). The testing effect is a well-studied phenomenon in which people retain
information better by simply being tested on it. Learners benefit from testing even if they are not provided feedback on their answers to the test, but
perform better yet if they are given feedback.
Cognitive psychologists also make a distinction between the types of
retrieval, recall, or recognition. Recalling information has long been known
to be more effective in learning than recognition, although recent research

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shows that multiple choice tests, which are recognition, can also be effective
in boosting learning (Glass & Sinha, 2013; Tulving, 1967). The challenging
part is identifying how to motivate students to become engaged in their
own learning, so that they appreciate the need to study for recall, instead of
recognition. Studying should focus on integrating or synthesizing information, rather than simply rereading or recognizing key terms. Furthermore,
expectations play an important role in remembering information. Expecting
to need information later makes that knowledge more accessible in the
future (Szupnar, McDermott, & Roediger, 2007). This concept is related to
the amount of effort viewed as sufficient in order to succeed. The effort
needed for learning should be at a “desirable” level of difficulty (Bjork &
Bjork, 2011) because the effort involved in learning can make recall more
likely.
Lastly, frequent retrieval also makes information more likely to transfer to
relevant situations (Carpenter, 2012) because the learning is deeper and recall
becomes increasingly automatic. In addition, many advanced learning technologies can involve an applied setting that might improve the likelihood
of transfer. For example, a serious game called Operation ARA engages students in knowledge transfer by having them identify flaws in research that
have been described in newspapers, blog posts, and other everyday outlets
(Halpern et al., 2012). Learners compete against other players and are given
immediate feedback about their performance.
PROVIDE FORMATIVE FEEDBACK
Quality feedback informs learners about why they got an item right or wrong
and not just the number or percentage correct. The repeated use of testing is a
feedback system; its superiority in promoting long-term learning and transfer is well documented (Carpenter, 2012). An effective learning system tests
students multiple times and provides quality feedback on performance. Tests
should be given according to a spaced schedule with increasing intervals. For
example, space between tests could occur two days, then two weeks, and
then two months after the initial learning to keep students engaged in the
material throughout the learning process, rather than at a single time during
the course (Glass & Sinha, 2013). Advanced learning technologies can make
repeated testing easier for instructors and can provide learners with immediate feedback that explains why they got an answer wrong and what the
right answer should be. Furthermore, some systems force students to reach
a predetermined level of competency on a topic before they can move on
to the next topics, instead of merely giving the learner feedback about their
performance.

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Overall, it is important to realize that leveraging technology does not necessarily mean using all available capabilities all the time. Typically, there is
much to be gained from simplicity. Interfaces can easily become cluttered,
which can cause distractions and overload. Certain screens, options, lessons,
and feedback are going to be ideal at different times. It can be the system’s
task to know when those ideal situations occur. Complexity on the learner’s
end should increase along with the abilities of the learner, not necessarily
with the abilities of technology.
IDEAL LEARNING TECHNOLOGY ATTRIBUTES
In general, the ideal learning technology will possess attributes that use the
principles described, and take advantage of the consistency of computers to
provide a quality environment for instruction. To be most effective, a learning
technology needs to consider learning research in its design. We already have
the ability to store large amounts of data about individual learners, including
rates of reading, response latencies, errors they make for each learning objective, level of engagement, length of responses to open-ended questions, and
much more. By combining data mining with education research, it is possible for a system to predict students’ scores on future exams and identify areas
in which each student or group of students needs more instruction or feedback. People learn at different rates, and the ideal solution is for teachers to
assess these needs, and make adjustments accordingly. The usual response
to a wide range of learning abilities and rates in a large classroom is to teach
to the hypothetical average student, a procedure that tends to lose both the
exceptionally talented and exceptionally slow learners. By automating this
process, all students can be instructed at their own level of understanding at
the same time, together or separately.
Educational data mining is a fast growing academic field. It is also becoming a popular approach to everyday living with “quantified life” advocates
urging people to keep careful quantified records of all aspects of their lives
so that they can discern health patterns, learn more efficiently, and achieve
almost any goal in their lives. Furthermore, relevant feedback can be given
at appropriate times in order to facilitate learning and catch errors, allowing a computerized program to recalibrate the level of instruction for each
student. One such program, SuperMemo (Wolf, 2008), is designed to assess
when students achieve a 90% probability of recall, and then queries the students with appropriate questions as a way of increasing retrieval strength. We
expect that the personalization of learning experiences is an emerging trend
that will yield high gains in learning, especially if students are monitored
correctly.

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Learning technologies need to personalize instruction by taking into
account individual needs from student-generated data. Automated personalization is one of the most hyped capabilities of computers in education. A
good system will help students overcome difficulties and frustrations often
associated with learning, such as short attention spans and learning materials that are based on faulty assumptions about what learners already know.
Advanced learning technologies should be able to flag misconceptions for
each learner and provide specifically targeted materials designed to correct
the misconceptions.
It is every professor’s dream—no more essay grading. Programs that offer
automatic analysis of essays are likely to be “the next big thing,” if (and it is
a big if) these grading programs can demonstrate that they are reliable and
valid, and users can get over the prejudicial belief that humans are inherently superior at this task. At this time, automated grading systems are far
from perfect, but many are as reliable as having two human experts grade
essays (Graesser & McNamara, 2012). These programs are still poor at recognizing novel metaphors, irony, sarcasm, and highly unusual high-quality
responses, but we suspect that the same can be said for human graders. There
are many automated grading systems, each designed with a particular specialization. One of the best analyzes content using Latent Semantic Analysis
(LSA, Landauer McNamara, Dennis, & Kintsch, 2007). The underlying principles are complex, but rest on an analysis of the number of adjacent and
nearby words that are found in natural language, a process known as n-gram
analysis. Automated grading systems are already being used in high stakes
testing (e.g., the writing assessment portion of the Graduate Management
Admission Test), and based on their successful use in this context, we believe
that automated grading will soon be more widely available for general educational use.
Advanced learning technologies can require students to respond in meaningful ways as they learn. There are programs that monitor student emotions during a learning session. For example, Linguistic Inquiry Word Count
(LIWC) designed by Pennebaker and his colleagues (Chung & Pennebaker,
2007; Pennebaker, Mehl, & Niederhoffer, 2003) can detect negative and positive emotions, so these sorts of programs could be used to screen for mental
health problems and for engagement in learning. Other programs can detect
when learners are bored and/or frustrated and can alter the learning materials in ways that increase engagement (D’Mello & Graesser, 2012).
Although it is impressive that affect can be automatically monitored,
positive interpersonal connections that often result between learners and
their teachers need to be maintained as we move increasingly toward
computer-mediated learning. Ask people about their favorite teacher and
you are likely to get a glowing response about the role that a special teacher

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played in the respondent’s life. There are intangibles in the teacher–learning
interaction that can create strong bonds and change the trajectory of the
student’s life. As we inevitably move into the use of advanced learning
technologies, we want to find ways to maintain that special relationship.
Advanced learning technologies can support and enhance small-class seminars in which each student responds to the teacher’s prompts, often in real
time. It remains to be seen if the same sort of relationship can be fostered as
we redefine terms like “live instruction” and “distance learning.” We believe
that it will be possible to create strong, positive student-teacher bonds and
that with careful design we can see more of these relationships develop, but
of course, we will not know until we have high quality programs designed
with this purpose in mind and enough data to support a conclusion.
Perhaps the most socially influential factors of an ideal system are the considerations of legitimacy and cost. Certificates of completion or some indicator of learning are becoming very important. Grades are given in traditional
courses, and degrees or diplomas are earned when certain requirements have
been met. Having tangible evidence of completion of a learning program is
not only motivating for learners, but is an indication of legitimacy. Many
free online courses offer some type of evidence of completion already, and
will continue to do so as online education increases in popularity. However,
keeping such systems cost-efficient can be a struggle.
Costs are always a focus for cash-strapped universities and even their more
affluent counterparts. A good learning technology must be a fiscally sound
investment. The benefits from a system must be worth the cost of implementation and upkeep. Luckily, the quick developmental pace of hardware has
permitted better accessibility and lower cost. Considerations of cost become
more vital as the learning platform becomes larger and more complex, and
when changes to the platform and technical support are a bigger task.
THE CAPABILITIES OF LEARNING TECHNOLOGIES
Many impressive technologies exist, but more research is needed to utilize
their educational potential. Three major areas of learning technology have
surfaced and gained solid traction recently: ITSs, serious games, and Massive
Online Open Classrooms (MOOCs). These tools have been in constant development for some time, but technological advancements are quickly making
them all much more effective.
INTELLIGENT TUTORING SYSTEMS AND PERSONALIZED INSTRUCTION
Emerging from the popularity of Big Data, which involves gathering,
storing, and analyzing large amounts of constantly changing information,

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automated assessment of student performance now allows for efficient
personalized instruction. By combining the disciplines of data mining and
computer science with pedagogy, learning systems can automatically make
adjustments to assist in student learning. ITSs are computerized learning
environments that are able to adapt to the differing needs of individuals and
provide appropriate feedback. Effectiveness of ITSs is an extensive area of
research, with several interesting findings that could guide further studies.
In a meta-analysis of 34 independent samples from 26 different studies conducted between 1997 and 2011, Steenbergen-Hu and Cooper (2013) found
that ITSs were at least as effective as regular classroom instruction for learning
mathematics at the K-12 level, and possibly slightly better in some cases. Positive learning effects were found to be greater for students from the general
population, as opposed to low achievers, suggesting that there may be some
minimal level of basic knowledge, skills, and motivation that are important
for computerized learning. Finally, the analysis also found that interventions
lasting longer than a year typically showed weaker effects than those lasting less than a year. Steenbergen-Hu and Cooper offer a few interpretations
of this counterintuitive finding. Motivation may be lost as novelty wears
off, researcher control over the implementation may have varied between
short and long interventions, or the studies themselves may have differed in
robustness of methods.
Other studies involving adaptive learning technologies have shown benefits to student learning. Walkington (2013) tested the effect of personalizing
the context in which math problems were presented on student ability to
write out algebraic equations. Students took a survey that assessed their
interests, and were then presented with math problems that were either
personalized to those outside interests, or were normal word problems.
Students who received personalized instruction demonstrated better performance on both types of problems. That is, students who learned in a context
in which they were interested were better able to express those problems
and later problems in equation form than were students who learned in a
non-personalized context. It is likely that a familiar or interesting context
provides students a scaffold in which to frame the math problems.
It should also be noted that different ITSs vary in the specific capabilities
they have, and the quality of their feedback. There are almost a limitless
number of variables to be studied. As education becomes more advanced,
automated learning systems become an even more promising area of
research. Success from these learning systems has paved the way for larger,
more extensive platforms that can handle more than just personalization of
instruction to individual students.

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SERIOUS GAMES
It has as long been a goal of educational technology to take advantage of the
immersion and challenge inherent in games. There is something very attractive about the combination of fun and learning which can be difficult to make
a reality. Serious games have already been shown to be effective in teaching materials such as scientific thinking (Halpern et al., 2012). Games have
the ability to get learners involved with the material, and promote an active
role in learning. They go beyond simple presentation of knowledge to get
students engaged in learning subject matter.
Games can make use of an imaginary, engaging context for students to
frame material. Instead of being bystanders in a passive learning process, students can become an active part of an adventure in which they play an exciting and meaningful role. Outcomes do not always have to be the same—they
can be dependent upon learner performance within the game. Dynamic content is a great channel through which to give learners feedback. With games,
feedback is a natural part of the process that will assist students as long as
they are attempting to perform well.
A recent meta-analysis of serious games found that game players learned
more and had better retention of the information they learned compared
to students who learned in a conventional learning method (Wouters, van
Nimwegan, van Oostendorp, & van der Spek, 2013). Surprisingly, the learners who played the games were not more motivated to learn. We believe that
this result is caused by the constant need to use high-level engagement in
learning games. Students pay attention and often spend more time on the
task of learning when engaged in games. The best games target deep learning
as the intended outcome, and deep learning is difficult work.
Mini games are also becoming popular. These games tend to be shorter,
web-based, and teach a single topic. Teaching simple concepts does not
require a full-fledged game, so independent developers can create mini
games more readily. These types of games are also inherently modular,
so they can be presented alongside associated material at any point. For
example, one of the authors of this chapter routinely uses a mini game in her
statistics classes (WISE, 2014). She plans out her lesson plan for a particular
topic (e.g., confidence intervals), and uses a small web-based game to
supplement the material presented in class. Students can manipulate graphs
and data until they understand the concept, and they can revisit the website
as often as they like. Students also get involved building these mini games,
which deepens their understanding of the material considerably.

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MOOCs (MASSIVE ONLINE OPEN CLASSROOMS)
With the advent of MOOCs, the Internet has created pathways for amazing
opportunities that would not have been feasible a decade ago. Hundreds of
thousands of students can now enroll and participate in a single, open course.
Once these courses have been developed and designed, much of the process
can be automated. Although the completion rates and student performance
may not be as high as traditional coursework, identifying factors that can
better motivate learners to complete these courses is an interesting applied
research area. One study found that the highest completion rate was 19%,
with many showing extremely low rates (Koutropoulos et al., 2012). With
such large sample sizes, there are huge amounts of data available to be mined
on the behaviors of students who enroll in this type of online course.
People who register for free online courses will have different goals
and expectations than students who enroll in traditional college courses.
Undoubtedly, there is interest even from learners who do not complete
the courses. Investigating retention rates based on individual profiles is a
promising area where data mining should be used for initial insight. Specific
hypotheses for further research can inform these increasingly popular
learning environments. In a recent systematic review of published research
articles about MOOCS, the authors concluded that the number of MOOCs
and research publications are increasing rapidly (Liyanagunawardena,
Adams, & Williams, 2013). Extrapolating from these data, we can expect
much more research on MOOCS in the next decade.
LEARNING WITH TECHNOLOGY IS THE FUTURE
The days of information being confined to libraries and a few well-read
minds have disappeared. Knowledge is now readily available to anyone
with an Internet connection, but effective methods of disseminating this
knowledge still need to be perfected. Several challenges exist for advanced
learning technologies. Research on learner intentions, motivations, and
other factors will assist in advancing best practices for new technologies.
The most current influential trends leverage the adaptability of technology,
focusing on accessible, individualized learning. Personalized lessons that
can be taken anywhere in the world are the future of learning technologies, but more research is needed to make them most effective. Much of
the infrastructure and technical capabilities already exist, but behavioral
research will need to hasten its pace if it wants to match the speed that these
innovative technologies are being produced. Research on these systems
is being undertaken and much more is still needed to ensure that these
technologies are effective and can meet the demands of our students.

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Roediger, H. L. I. I. I., & Karpicke, J. D. (2006). The power of testing memory: Basic
research and implications for educational practice. Psychological Science, 1, 181–210.
Steenbergen-Hu, S., & Cooper, H. (2013). A meta-analysis of the effectiveness of
intelligent tutoring systems on K-12 students’ mathematical learning. Journal of
Educational Psychology, 105(4), 970–987. doi:10.1037/a0032447
Sweller, J., van Merrienboer, J. J. G., & Paas, F. G. W. C. (1998). Cognitive architecture and instructional design. Educational Psychology, 10(3), 251–296. doi:10.1023/
A:1022193728205
Szpunar, K. K., McDermott, K. B., & Roediger, H. L., III, (2007). Expectation of a
final cumulative test enhances long-term retention. Memory & Cognition, 35(5),
1007–1013.
Tulving, E. (1967). The effects of presentation and recall of material in free-recall
learning. Journal of Verbal Learning and Verbal Behavior, 6, 175–184.
Walkington, C. A. (2013). Using adaptive learning technologies to personalize
instruction to student interests: The impact of relevant contexts on performance and learning outcomes. Journal of Educational Psychology, 105(4), 932–945.
doi:10.1037/a0031882
WISE Homepage. (2014). Web interface for statistics education. Retrieved from
http://wise.cgu.edu/
Wolf, G. (2008). Want to remember everything you’ll ever learn? Surrender to
this algorithm. Wired Magazine, 16(5) Retrieved from http://www.wired.com/
medtech/health/magazine/16-05/ff_wozniak?currentPage=all
Wouters, P., van Nimwegen, C., van Oostendorp, H., & van der Spek, E. D. (2013). A
meta-analysis of the cognitive and motivational effects of serious games. Journal
of Educational Psychology, 105(2), 249–265. doi:10.1037/a0031311

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FURTHER READING
Mayer, R. E. (2009). Multimedia learning (2nd ed.). New York, NY: Cambridge University Press.
Steenbergen-Hu, S., & Cooper, H. (2013). A meta-analysis of the effectiveness of
intelligent tutoring systems on K-12 students’ mathematical learning. Journal of
Educational Psychology, 105(4), 970–987. doi:10.1037/a0032447

CHRISTOPHER S. PENTONEY SHORT BIOGRAPHY
Christopher S. Pentoney is a graduate student in Applied Cognitive Psychology at Claremont Graduate University. His research interests are in the
application of statistics and data mining in learning technologies. His current projects involve the development of learning software for statistics, and
automating the classification of simple and difficult text.

DIANE F. HALPERN SHORT BIOGRAPHY
Diane F. Halpern is the Dean of Social Sciences at the Minerva Schools at
KGI. She is past president of the American Psychological Association. Diane
has published over 20 books including, Thought and Knowledge: An Introduction to Critical Thinking (5th ed.) and Sex Differences in Cognitive Abilities (4th ed.). Diane’s recent projects include the development of Operation
ARA, a computerized game that teaches critical thinking and scientific reasoning (with Keith Millis and Art Graesser) and the Halpern Critical Thinking Assessment (Schuhfried Publishers) that uses multiple response formats,
which allow test takers to demonstrate their ability to think about everyday
topics using both constructed response and recognition formats.

HEATHER A. BUTLER SHORT BIOGRAPHY
Heather A. Butler is an assistant professor in the psychology department at
California State University Dominguez Hills. She has a number of research
interests that are grounded in human cognition (critical thinking, advanced
learning technologies, cognitive bias in the legal system). As a graduate student, Heather was involved in the development Operation ARA, a serious
game that teaches scientific reasoning. She is currently pursuing grant funding to develop a new serious game that would improve the critical thinking
skills of college students.

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15

RELATED ESSAYS
The Psychological Impacts of Cyberlife Engagement (Psychology), Virginia S.
Y. Kwan and Jessica E. Bodford
Rationalization of Higher Education (Sociology), Tressie McMillan Cottom
and Gaye Tuchman
Education in an Open Informational World (Educ), Marlene Scardamalia and
Carl Bereiter
Higher Education: A Field in Ferment (Sociology), W. Richard Scott; The Impact of Learning Technologies
on Higher Education
CHRISTOPHER S. PENTONEY, DIANE F. HALPERN, and HEATHER A. BUTLER

Abstract
Demand for higher education has created a need for learning technologies that can
accommodate the individualized needs of an increasing number of students. Thinking, learning, and memory have been studied extensively in their own right, but additional research on these topics in conjunction with advanced learning technologies
is needed. Developers of computerized tutoring systems, massive online courses,
and educational games will benefit from forward-thinking studies. Limitations are
constantly being lifted, and research must increase in pace to ensure the integrity of
upcoming learning technologies.

HOW ADVANCED LEARNING TECHNOLOGIES
ARE REINVENTING HIGHER EDUCATION
If all of the hype about reinventing, redefining, revolutionizing, and reinvigorating education is to be believed, we can expect fundamental changes in how
we teach and learn in the near future. Advanced learning technologies are the
engine fueling change. Automated assessments, customized feedback, and
gigantic online classrooms have already become reality to accommodate the
growing demand for flexible learning environments and a higher education
system that is more efficient, affordable, and accessible. A broad array of tools
for improving the way we “do” education are being developed as advances
in education and technology continuously shape each other.
As with any rapidly changing field, it can be difficult to discern the difference between substance and hype. The best of the advanced learning technologies are “created by designers who have a substantial theoretical and
empirical understanding of learners, learning, and the targeted subject matter” (Aleven, Beal, & Graesser, 2013, p. 929). These new technologies offer the
promise of achieving the gold standard for learning—creating deep knowledge that persists over time and transfers to new situations. The field is still in
Emerging Trends in the Social and Behavioral Sciences. Edited by Robert Scott and Stephen Kosslyn.
© 2015 John Wiley & Sons, Inc. ISBN 978-1-118-90077-2.

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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

its infancy, or perhaps toddlerhood, and thus currently offers more promise
of future development than evidence.
There is a great variety of advanced learning technologies—automated
assessment, personalized instruction, distance learning via proprietary
platforms, massive online open courses, intelligent tutoring systems (ITSs),
serious games (described later), and content-related activities such as
simulations and interactive reading, to name a few. It is very challenging to
design these emerging technologies, and venture capitalists are beginning
to pour resources into their development with the hope of striking it rich.
The excitement around the possibility of creating new and better ways of
learning is a welcome change to the status quo. There are numerous research
and practical issues to consider, and the hype surrounding new capabilities
should be grounded in realistic expectations.
The basic model of education has remained unchanged since the time
of Christ (and much earlier), who is often depicted talking to a group of
people to instruct them in the ways of religion. Similarly, teachers stand
before a group of students telling them what they need to know, stopping
occasionally for questions and discussions, but most of the time the communication is from teacher to learner. We can now envision better ways.
Rather than using a traditional classroom format, computers have the ability
to adapt to hordes of individual students located around the planet. As
access to the Internet becomes closer to a utility than a privilege for many
people, knowledge becomes more readily available. It was not long ago
when much of the world’s accumulated knowledge resided on the shelves
of elite libraries where only a privileged few had access. Advanced learning
technologies have democratized access to knowledge, which is a huge
sociological/political change that disseminates the power of knowledge
more widely than ever before in history. With the speed that progress is
being made, there is a need to keep the new technologies grounded in what
we know about how people learn, as well as a need for high-quality research
in the expanding field of technology-mediated education.
OVERVIEW OF BASIC LEARNING PRINCIPLES
Learning technologies should be informed by research that has identified
best practices in instruction. Ironically, the so-called “curse of knowledge”
can lead even innovative and motivated educational designers astray. This
phrase refers to the commonly experienced phenomenon in which experts
design systems that can only be used by other experts because they assume
that a theory or skill that is well known to them will be easily grasped by
novices. Rae-Dupree (2007, para. 7) describes the curse of knowledge this
way, “It’s why engineers design products ultimately useful only to other

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engineers. It’s why managers have trouble convincing the rank and file to
adopt new processes. And it’s why the advertising world struggles to convey
commercial messages to consumers.” We list here a few of the most salient
learning principles that need to be considered when designing new learning
technologies. An exhaustive list is beyond the scope of this essay, so we have
chosen to include those that we believe offer the greatest benefits.
PRESENT INFORMATION IN A WAY THAT REDUCES COGNITIVE LOAD
One of the advantages of learning technologies is that they are able to present
information to students in a variety of formats. For example, interactive
e-books are capable of containing embedded videos, audio recordings,
games, quizzes, interactive diagrams, and links to supplemental reading.
Although there are undeniable benefits to the use of these technologies,
the way in which information is presented to students affects their mastery
of that knowledge. Associated ideas should be presented according to
guidelines that facilitate connections in a meaningful way. Research on
contiguity effects shows that when text and images are related to each other
they should be placed near each other, in both space and in time (Mayer,
2009). For example, when learners have to search for mathematical diagrams
associated with a formula, the demands on working memory, known as
cognitive load, are increased and learning suffers (Ginns, 2006). The idea of
placing related pieces of information in close proximity to each other seems
like an obvious concept, but it can be easily overlooked.
Optimal learning materials involve more than visual displays. Information
is better remembered when it is delivered in multiple modes, such as
through auditory and visual methods combined (Mayer, 2009; Moreno &
Valdez, 2005), and in multiple formats such as graphs, animations, text, and
their combinations. Using varied modes of presentation enables more routes
for retrieval of that information later on, and the ability to manipulate the
various representations provides a level of learner engagement that would
not be possible without the new technologies that support it. For example,
when learners can physically manipulate kinematic graphs, they have a
better understanding of the underlying principles than those who watched
others manipulate the graphs (Anastopoulou, Sharples, & Baber, 2011).
Management of cognitive load is vital when considering how to present
information to learners.
DIVIDE INFORMATION INTO MANAGEABLE UNITS
The contiguity principle is not the only way to reduce the cognitive load of
learners. All pedagogical designs need to incorporate the fact that humans

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have a finite amount of working memory. Mayer and Moreno (2003) suggest that large amounts of new information be presented in discrete units
to reduce cognitive load, rather than all together at the same time. Learners
need to be allowed to process subunits of information before making connections between them. Furthermore, providing too much material at one
time or presenting irrelevant information can be harmful to learning. Certain information and images might be visually appealing, but if they are not
directly related to the topic being studied, they should be excluded (Kalyuga,
Chandler, & Sweller, 1999; Mayer, 2009).
Theorists usually differentiate cognitive load into three components: intrinsic (necessary for learning), extraneous (irrelevant to learning), and germane
(related to schema construction) (Sweller, van Merrienboer, & Paas, 1998).
For example, if a learner is having difficulty using educational software, the
additional strain associated with the operation of the software is irrelevant
to the material or skills being learned. A well-designed program will have
a low level of extraneous cognitive load, which means that the operation of
the program (e.g., key strokes needed to manipulate graphs or the way questions are worded) is intuitive. It will also have an optimal level of germane
load—the integration of information to-be-learned is neither too easy nor too
difficult for the learner. These three distinctions have been supported by confirmatory factor analysis (Leppink, Paas, Van der Vleuten, Van Gog, & Van
Merrienboer, 2013). It is important that the advanced learning technology not
be so complicated to operate that it interferes with learning.
BOOST LEARNING WITH REPEATED RETRIEVAL
The best way to make learning “stick” is with practice that involves repeated
retrieval from memory (Glass, 2009; Little, Bjork, Bjork, & Angello, 2012).
The basic idea underlying the superiority of repeated retrieval of information
from memory is that each item in memory has a probability associated with
its likelihood of being recalled. With repeated retrieval, the memory trace is
strengthened and information in memory becomes more likely to be recalled.
This principle has been dubbed the “testing effect” because retrieval usually
occurs in the context of a test. Broadly, findings from research on testing support giving students multiple, frequent examinations (Roediger & Karpicke,
2006). The testing effect is a well-studied phenomenon in which people retain
information better by simply being tested on it. Learners benefit from testing even if they are not provided feedback on their answers to the test, but
perform better yet if they are given feedback.
Cognitive psychologists also make a distinction between the types of
retrieval, recall, or recognition. Recalling information has long been known
to be more effective in learning than recognition, although recent research

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shows that multiple choice tests, which are recognition, can also be effective
in boosting learning (Glass & Sinha, 2013; Tulving, 1967). The challenging
part is identifying how to motivate students to become engaged in their
own learning, so that they appreciate the need to study for recall, instead of
recognition. Studying should focus on integrating or synthesizing information, rather than simply rereading or recognizing key terms. Furthermore,
expectations play an important role in remembering information. Expecting
to need information later makes that knowledge more accessible in the
future (Szupnar, McDermott, & Roediger, 2007). This concept is related to
the amount of effort viewed as sufficient in order to succeed. The effort
needed for learning should be at a “desirable” level of difficulty (Bjork &
Bjork, 2011) because the effort involved in learning can make recall more
likely.
Lastly, frequent retrieval also makes information more likely to transfer to
relevant situations (Carpenter, 2012) because the learning is deeper and recall
becomes increasingly automatic. In addition, many advanced learning technologies can involve an applied setting that might improve the likelihood
of transfer. For example, a serious game called Operation ARA engages students in knowledge transfer by having them identify flaws in research that
have been described in newspapers, blog posts, and other everyday outlets
(Halpern et al., 2012). Learners compete against other players and are given
immediate feedback about their performance.
PROVIDE FORMATIVE FEEDBACK
Quality feedback informs learners about why they got an item right or wrong
and not just the number or percentage correct. The repeated use of testing is a
feedback system; its superiority in promoting long-term learning and transfer is well documented (Carpenter, 2012). An effective learning system tests
students multiple times and provides quality feedback on performance. Tests
should be given according to a spaced schedule with increasing intervals. For
example, space between tests could occur two days, then two weeks, and
then two months after the initial learning to keep students engaged in the
material throughout the learning process, rather than at a single time during
the course (Glass & Sinha, 2013). Advanced learning technologies can make
repeated testing easier for instructors and can provide learners with immediate feedback that explains why they got an answer wrong and what the
right answer should be. Furthermore, some systems force students to reach
a predetermined level of competency on a topic before they can move on
to the next topics, instead of merely giving the learner feedback about their
performance.

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Overall, it is important to realize that leveraging technology does not necessarily mean using all available capabilities all the time. Typically, there is
much to be gained from simplicity. Interfaces can easily become cluttered,
which can cause distractions and overload. Certain screens, options, lessons,
and feedback are going to be ideal at different times. It can be the system’s
task to know when those ideal situations occur. Complexity on the learner’s
end should increase along with the abilities of the learner, not necessarily
with the abilities of technology.
IDEAL LEARNING TECHNOLOGY ATTRIBUTES
In general, the ideal learning technology will possess attributes that use the
principles described, and take advantage of the consistency of computers to
provide a quality environment for instruction. To be most effective, a learning
technology needs to consider learning research in its design. We already have
the ability to store large amounts of data about individual learners, including
rates of reading, response latencies, errors they make for each learning objective, level of engagement, length of responses to open-ended questions, and
much more. By combining data mining with education research, it is possible for a system to predict students’ scores on future exams and identify areas
in which each student or group of students needs more instruction or feedback. People learn at different rates, and the ideal solution is for teachers to
assess these needs, and make adjustments accordingly. The usual response
to a wide range of learning abilities and rates in a large classroom is to teach
to the hypothetical average student, a procedure that tends to lose both the
exceptionally talented and exceptionally slow learners. By automating this
process, all students can be instructed at their own level of understanding at
the same time, together or separately.
Educational data mining is a fast growing academic field. It is also becoming a popular approach to everyday living with “quantified life” advocates
urging people to keep careful quantified records of all aspects of their lives
so that they can discern health patterns, learn more efficiently, and achieve
almost any goal in their lives. Furthermore, relevant feedback can be given
at appropriate times in order to facilitate learning and catch errors, allowing a computerized program to recalibrate the level of instruction for each
student. One such program, SuperMemo (Wolf, 2008), is designed to assess
when students achieve a 90% probability of recall, and then queries the students with appropriate questions as a way of increasing retrieval strength. We
expect that the personalization of learning experiences is an emerging trend
that will yield high gains in learning, especially if students are monitored
correctly.

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Learning technologies need to personalize instruction by taking into
account individual needs from student-generated data. Automated personalization is one of the most hyped capabilities of computers in education. A
good system will help students overcome difficulties and frustrations often
associated with learning, such as short attention spans and learning materials that are based on faulty assumptions about what learners already know.
Advanced learning technologies should be able to flag misconceptions for
each learner and provide specifically targeted materials designed to correct
the misconceptions.
It is every professor’s dream—no more essay grading. Programs that offer
automatic analysis of essays are likely to be “the next big thing,” if (and it is
a big if) these grading programs can demonstrate that they are reliable and
valid, and users can get over the prejudicial belief that humans are inherently superior at this task. At this time, automated grading systems are far
from perfect, but many are as reliable as having two human experts grade
essays (Graesser & McNamara, 2012). These programs are still poor at recognizing novel metaphors, irony, sarcasm, and highly unusual high-quality
responses, but we suspect that the same can be said for human graders. There
are many automated grading systems, each designed with a particular specialization. One of the best analyzes content using Latent Semantic Analysis
(LSA, Landauer McNamara, Dennis, & Kintsch, 2007). The underlying principles are complex, but rest on an analysis of the number of adjacent and
nearby words that are found in natural language, a process known as n-gram
analysis. Automated grading systems are already being used in high stakes
testing (e.g., the writing assessment portion of the Graduate Management
Admission Test), and based on their successful use in this context, we believe
that automated grading will soon be more widely available for general educational use.
Advanced learning technologies can require students to respond in meaningful ways as they learn. There are programs that monitor student emotions during a learning session. For example, Linguistic Inquiry Word Count
(LIWC) designed by Pennebaker and his colleagues (Chung & Pennebaker,
2007; Pennebaker, Mehl, & Niederhoffer, 2003) can detect negative and positive emotions, so these sorts of programs could be used to screen for mental
health problems and for engagement in learning. Other programs can detect
when learners are bored and/or frustrated and can alter the learning materials in ways that increase engagement (D’Mello & Graesser, 2012).
Although it is impressive that affect can be automatically monitored,
positive interpersonal connections that often result between learners and
their teachers need to be maintained as we move increasingly toward
computer-mediated learning. Ask people about their favorite teacher and
you are likely to get a glowing response about the role that a special teacher

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played in the respondent’s life. There are intangibles in the teacher–learning
interaction that can create strong bonds and change the trajectory of the
student’s life. As we inevitably move into the use of advanced learning
technologies, we want to find ways to maintain that special relationship.
Advanced learning technologies can support and enhance small-class seminars in which each student responds to the teacher’s prompts, often in real
time. It remains to be seen if the same sort of relationship can be fostered as
we redefine terms like “live instruction” and “distance learning.” We believe
that it will be possible to create strong, positive student-teacher bonds and
that with careful design we can see more of these relationships develop, but
of course, we will not know until we have high quality programs designed
with this purpose in mind and enough data to support a conclusion.
Perhaps the most socially influential factors of an ideal system are the considerations of legitimacy and cost. Certificates of completion or some indicator of learning are becoming very important. Grades are given in traditional
courses, and degrees or diplomas are earned when certain requirements have
been met. Having tangible evidence of completion of a learning program is
not only motivating for learners, but is an indication of legitimacy. Many
free online courses offer some type of evidence of completion already, and
will continue to do so as online education increases in popularity. However,
keeping such systems cost-efficient can be a struggle.
Costs are always a focus for cash-strapped universities and even their more
affluent counterparts. A good learning technology must be a fiscally sound
investment. The benefits from a system must be worth the cost of implementation and upkeep. Luckily, the quick developmental pace of hardware has
permitted better accessibility and lower cost. Considerations of cost become
more vital as the learning platform becomes larger and more complex, and
when changes to the platform and technical support are a bigger task.
THE CAPABILITIES OF LEARNING TECHNOLOGIES
Many impressive technologies exist, but more research is needed to utilize
their educational potential. Three major areas of learning technology have
surfaced and gained solid traction recently: ITSs, serious games, and Massive
Online Open Classrooms (MOOCs). These tools have been in constant development for some time, but technological advancements are quickly making
them all much more effective.
INTELLIGENT TUTORING SYSTEMS AND PERSONALIZED INSTRUCTION
Emerging from the popularity of Big Data, which involves gathering,
storing, and analyzing large amounts of constantly changing information,

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automated assessment of student performance now allows for efficient
personalized instruction. By combining the disciplines of data mining and
computer science with pedagogy, learning systems can automatically make
adjustments to assist in student learning. ITSs are computerized learning
environments that are able to adapt to the differing needs of individuals and
provide appropriate feedback. Effectiveness of ITSs is an extensive area of
research, with several interesting findings that could guide further studies.
In a meta-analysis of 34 independent samples from 26 different studies conducted between 1997 and 2011, Steenbergen-Hu and Cooper (2013) found
that ITSs were at least as effective as regular classroom instruction for learning
mathematics at the K-12 level, and possibly slightly better in some cases. Positive learning effects were found to be greater for students from the general
population, as opposed to low achievers, suggesting that there may be some
minimal level of basic knowledge, skills, and motivation that are important
for computerized learning. Finally, the analysis also found that interventions
lasting longer than a year typically showed weaker effects than those lasting less than a year. Steenbergen-Hu and Cooper offer a few interpretations
of this counterintuitive finding. Motivation may be lost as novelty wears
off, researcher control over the implementation may have varied between
short and long interventions, or the studies themselves may have differed in
robustness of methods.
Other studies involving adaptive learning technologies have shown benefits to student learning. Walkington (2013) tested the effect of personalizing
the context in which math problems were presented on student ability to
write out algebraic equations. Students took a survey that assessed their
interests, and were then presented with math problems that were either
personalized to those outside interests, or were normal word problems.
Students who received personalized instruction demonstrated better performance on both types of problems. That is, students who learned in a context
in which they were interested were better able to express those problems
and later problems in equation form than were students who learned in a
non-personalized context. It is likely that a familiar or interesting context
provides students a scaffold in which to frame the math problems.
It should also be noted that different ITSs vary in the specific capabilities
they have, and the quality of their feedback. There are almost a limitless
number of variables to be studied. As education becomes more advanced,
automated learning systems become an even more promising area of
research. Success from these learning systems has paved the way for larger,
more extensive platforms that can handle more than just personalization of
instruction to individual students.

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SERIOUS GAMES
It has as long been a goal of educational technology to take advantage of the
immersion and challenge inherent in games. There is something very attractive about the combination of fun and learning which can be difficult to make
a reality. Serious games have already been shown to be effective in teaching materials such as scientific thinking (Halpern et al., 2012). Games have
the ability to get learners involved with the material, and promote an active
role in learning. They go beyond simple presentation of knowledge to get
students engaged in learning subject matter.
Games can make use of an imaginary, engaging context for students to
frame material. Instead of being bystanders in a passive learning process, students can become an active part of an adventure in which they play an exciting and meaningful role. Outcomes do not always have to be the same—they
can be dependent upon learner performance within the game. Dynamic content is a great channel through which to give learners feedback. With games,
feedback is a natural part of the process that will assist students as long as
they are attempting to perform well.
A recent meta-analysis of serious games found that game players learned
more and had better retention of the information they learned compared
to students who learned in a conventional learning method (Wouters, van
Nimwegan, van Oostendorp, & van der Spek, 2013). Surprisingly, the learners who played the games were not more motivated to learn. We believe that
this result is caused by the constant need to use high-level engagement in
learning games. Students pay attention and often spend more time on the
task of learning when engaged in games. The best games target deep learning
as the intended outcome, and deep learning is difficult work.
Mini games are also becoming popular. These games tend to be shorter,
web-based, and teach a single topic. Teaching simple concepts does not
require a full-fledged game, so independent developers can create mini
games more readily. These types of games are also inherently modular,
so they can be presented alongside associated material at any point. For
example, one of the authors of this chapter routinely uses a mini game in her
statistics classes (WISE, 2014). She plans out her lesson plan for a particular
topic (e.g., confidence intervals), and uses a small web-based game to
supplement the material presented in class. Students can manipulate graphs
and data until they understand the concept, and they can revisit the website
as often as they like. Students also get involved building these mini games,
which deepens their understanding of the material considerably.

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MOOCs (MASSIVE ONLINE OPEN CLASSROOMS)
With the advent of MOOCs, the Internet has created pathways for amazing
opportunities that would not have been feasible a decade ago. Hundreds of
thousands of students can now enroll and participate in a single, open course.
Once these courses have been developed and designed, much of the process
can be automated. Although the completion rates and student performance
may not be as high as traditional coursework, identifying factors that can
better motivate learners to complete these courses is an interesting applied
research area. One study found that the highest completion rate was 19%,
with many showing extremely low rates (Koutropoulos et al., 2012). With
such large sample sizes, there are huge amounts of data available to be mined
on the behaviors of students who enroll in this type of online course.
People who register for free online courses will have different goals
and expectations than students who enroll in traditional college courses.
Undoubtedly, there is interest even from learners who do not complete
the courses. Investigating retention rates based on individual profiles is a
promising area where data mining should be used for initial insight. Specific
hypotheses for further research can inform these increasingly popular
learning environments. In a recent systematic review of published research
articles about MOOCS, the authors concluded that the number of MOOCs
and research publications are increasing rapidly (Liyanagunawardena,
Adams, & Williams, 2013). Extrapolating from these data, we can expect
much more research on MOOCS in the next decade.
LEARNING WITH TECHNOLOGY IS THE FUTURE
The days of information being confined to libraries and a few well-read
minds have disappeared. Knowledge is now readily available to anyone
with an Internet connection, but effective methods of disseminating this
knowledge still need to be perfected. Several challenges exist for advanced
learning technologies. Research on learner intentions, motivations, and
other factors will assist in advancing best practices for new technologies.
The most current influential trends leverage the adaptability of technology,
focusing on accessible, individualized learning. Personalized lessons that
can be taken anywhere in the world are the future of learning technologies, but more research is needed to make them most effective. Much of
the infrastructure and technical capabilities already exist, but behavioral
research will need to hasten its pace if it wants to match the speed that these
innovative technologies are being produced. Research on these systems
is being undertaken and much more is still needed to ensure that these
technologies are effective and can meet the demands of our students.

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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

FURTHER READING
Mayer, R. E. (2009). Multimedia learning (2nd ed.). New York, NY: Cambridge University Press.
Steenbergen-Hu, S., & Cooper, H. (2013). A meta-analysis of the effectiveness of
intelligent tutoring systems on K-12 students’ mathematical learning. Journal of
Educational Psychology, 105(4), 970–987. doi:10.1037/a0032447

CHRISTOPHER S. PENTONEY SHORT BIOGRAPHY
Christopher S. Pentoney is a graduate student in Applied Cognitive Psychology at Claremont Graduate University. His research interests are in the
application of statistics and data mining in learning technologies. His current projects involve the development of learning software for statistics, and
automating the classification of simple and difficult text.

DIANE F. HALPERN SHORT BIOGRAPHY
Diane F. Halpern is the Dean of Social Sciences at the Minerva Schools at
KGI. She is past president of the American Psychological Association. Diane
has published over 20 books including, Thought and Knowledge: An Introduction to Critical Thinking (5th ed.) and Sex Differences in Cognitive Abilities (4th ed.). Diane’s recent projects include the development of Operation
ARA, a computerized game that teaches critical thinking and scientific reasoning (with Keith Millis and Art Graesser) and the Halpern Critical Thinking Assessment (Schuhfried Publishers) that uses multiple response formats,
which allow test takers to demonstrate their ability to think about everyday
topics using both constructed response and recognition formats.

HEATHER A. BUTLER SHORT BIOGRAPHY
Heather A. Butler is an assistant professor in the psychology department at
California State University Dominguez Hills. She has a number of research
interests that are grounded in human cognition (critical thinking, advanced
learning technologies, cognitive bias in the legal system). As a graduate student, Heather was involved in the development Operation ARA, a serious
game that teaches scientific reasoning. She is currently pursuing grant funding to develop a new serious game that would improve the critical thinking
skills of college students.

The Impact of Learning Technologies on Higher Education

15

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