Educational data mining (EDM) describes a research field concerned with the application of data mining, machine learning and statistics to information generated from educational settings (e.g., universities and intelligent tutoring systems).
At a high level, the field seeks to develop and improve methods for
exploring this data, which often has multiple levels of meaningful
hierarchy, in order to discover new insights about how people learn in
the context of such settings. In doing so, EDM has contributed to theories of learning investigated by researchers in educational psychology and the learning sciences. The field is closely tied to that of learning analytics, and the two have been compared and contrasted.
Definition
Educational
data mining refers to techniques, tools, and research designed for
automatically extracting meaning from large repositories of data
generated by or related to people's learning activities in educational settings. Quite often, this data is extensive, fine-grained, and precise. For example, several learning management systems (LMSs) track information such as when each student accessed each learning object,
how many times they accessed it, and how many minutes the learning
object was displayed on the user's computer screen. As another example, intelligent tutoring systems
record data every time a learner submits a solution to a problem; they
may collect the time of the submission, whether or not the solution
matches the expected solution, the amount of time that has passed since
the last submission, the order in which solution components were entered
into the interface, etc. The precision of this data is such that even a
fairly short session with a computer-based learning environment (e.g., 30 minutes) may produce a large amount of process data for analysis.
In other cases, the data is less fine-grained. For example, a student's universitytranscript may contain a temporally ordered list of courses taken by the student, the grade that the student earned in each course, and when the student selected or changed his or her academic major.
EDM leverages both types of data to discover meaningful information
about different types of learners and how they learn, the structure of domain knowledge,
and the effect of instructional strategies embedded within various
learning environments. These analyses provide new information that would
be difficult to discern by looking at the raw data.
For example, analyzing data from an LMS may reveal a relationship
between the learning objects that a student accessed during the course
and their final course grade. Similarly, analyzing student transcript
data may reveal a relationship between a student's grade in a particular
course and their decision to change their academic major. Such
information provides insight into the design of learning environments,
which allows students, teachers, school administrators, and educational
policy makers to make informed decisions about how to interact with,
provide, and manage educational resources.
History
While the analysis of educational data is not itself a new practice, recent advances in educational technology,
including the increase in computing power and the ability to log
fine-grained data about students' use of a computer-based learning
environment, have led to an increased interest in developing techniques
for analyzing the large amounts of data generated in educational
settings. This interest translated into a series of EDM workshops held
from 2000 to 2007 as part of several international research conferences.
In 2008, a group of researchers established what has become an annual
international research conference on EDM, the first of which took place
in Montreal, Quebec, Canada.
With the introduction of public educational data repositories in 2008, such as the Pittsburgh Science of Learning Centre's (PSLC) DataShop and the National Center for Education Statistics (NCES), public data sets have made educational data mining more accessible and feasible, contributing to its growth.
Goals
Ryan S. Baker and Kalina Yacef identified the following four goals of EDM:
Predicting students' future learning behavior – With the use of student modeling,
this goal can be achieved by creating student models that incorporate
the learner's characteristics, including detailed information such as
their knowledge, behaviours and motivation to learn. The user experience of the learner and their overall satisfaction with learning are also measured.
Discovering or improving domain models – Through the various
methods and applications of EDM, discovery of new and improvements to
existing models is possible. Examples include illustrating the
educational content to engage learners and determining optimal
instructional sequences to support the student's learning style.
Studying the effects of educational support that can be achieved through learning systems.
Advancing scientific knowledge about learning and learners by building and incorporating student models, the field of EDM research and the technology and software used.
Users and stakeholders
There are four main users and stakeholders involved with educational data mining. These include:
Learners – Learners are interested in understanding student needs and methods to improve the learner's experience and performance.
For example, learners can also benefit from the discovered knowledge by
using the EDM tools to suggest activities and resources that they can
use based on their interactions with the online learning tool and insights from past or similar learners. For younger learners, educational data mining can also inform parents about their child's learning progress.
It is also necessary to effectively group learners in an online
environment. The challenge is to learn these groups based on the complex
data as well as develop actionable models to interpret these groups.
Educators – Educators attempt to understand the learning process and the methods they can use to improve their teaching methods. Educators can use the applications of EDM to determine how to organize and structure the curriculum, the best methods to deliver course information and the tools to use to engage their learners for optimal learning outcomes.
In particular, the distillation of data for human judgment technique
provides an opportunity for educators to benefit from EDM because it
enables educators to quickly identify behavioural patterns, which can
support their teaching methods during the duration of the course or to
improve future courses. Educators can determine indicators that show
student satisfaction and engagement of course material, and also monitor
learning progress.
Researchers – Researchers focus on the development and the evaluation of data mining techniques for effectiveness. A yearly international conference for researchers began in 2008, followed by the establishment of the Journal of Educational Data Mining
in 2009. The wide range of topics in EDM ranges from using data mining
to improve institutional effectiveness to student performance.
Administrators – Administrators are responsible for allocating the resources for implementation in institutions.
As institutions are increasingly held responsible for student success,
the administering of EDM applications are becoming more common in
educational settings. Faculty and advisors are becoming more proactive
in identifying and addressing at-risk students. However, it is sometimes
a challenge to get the information to the decision makers to administer
the application in a timely and efficient manner.
Phases
As
research in the field of educational data mining has continued to grow, a
myriad of data mining techniques have been applied to a variety of
educational contexts. In each case, the goal is to translate raw data
into meaningful information about the learning process in order to make
better decisions about the design and trajectory of a learning
environment. Thus, EDM generally consists of four phases:
Validated relationships are applied to make predictions about future events in the learning environment.
Predictions are used to support decision-making processes and policy decisions.
During phases 3 and 4, data is often visualized or in some other way distilled for human judgment. A large amount of research has been conducted in best practices for visualizing data.
Main approaches
Of the general categories of methods mentioned, prediction, clustering and relationship mining are considered universal methods across all types of data mining; however, Discovery with Models and Distillation of Data for Human Judgment are considered more prominent approaches within educational data mining.
Discovery with models
In the Discovery with Model method, a model is developed via prediction, clustering or by human reasoning knowledge engineering and then used as a component in another analysis, namely in prediction and relationship mining. In the prediction method use, the created model's predictions are used to predict a new variable. For the use of relationship mining, the created model enables the analysis between new predictions and additional variables in the study. In many cases, discovery with models uses validated prediction models that have proven generalizability across contexts.
Key applications of this method include discovering relationships between student behaviors, characteristics and contextual variables in the learning environment.
Further discovery of broad and specific research questions across a
wide range of contexts can also be explored using this method.
Distillation of data for human judgment
Humans can make inferences about data that may be beyond the scope in which an automated data mining method provides. For the use of education data mining, data is distilled for human judgment for two key purposes, identification and classification.
For the purpose of identification,
data is distilled to enable humans to identify well-known patterns,
which may otherwise be difficult to interpret. For example, the learning curve, classic to educational studies, is a pattern that clearly reflects the relationship between learning and experience over time.
Data is also distilled for the purposes of classifying
features of data, which for educational data mining, is used to support
the development of the prediction model. Classification helps expedite
the development of the prediction model, tremendously.
The goal of this method is to summarize and present the information in a useful, interactive and visually appealing way in order to understand the large amounts of education data and to support decision making.
In particular, this method is beneficial to educators in understanding
usage information and effectiveness in course activities.
Key applications for the distillation of data for human judgment
include identifying patterns in student learning, behavior,
opportunities for collaboration and labeling data for future uses in prediction models.
Applications
A list of the primary applications of EDM is provided by Cristobal Romero and Sebastian Ventura. In their taxonomy, the areas of EDM application are:
Constructing courseware – EDM can be applied to course management systems such as open source Moodle.
Moodle contains usage data that includes various activities by users
such as test results, amount of readings completed and participation in discussion forums.
Data mining tools can be used to customize learning activities for each
user and adapt the pace in which the student completes the course. This
is in particularly beneficial for online courses with varying levels of competency.
Planning and scheduling
New research on mobile
learning environments also suggests that data mining can be useful.
Data mining can be used to help provide personalized content to mobile
users, despite the differences in managing content between mobile devices and standard PCs and web browsers.
New EDM applications will focus on allowing non-technical users use and engage in data mining tools and activities, making data collection and processing more accessible for all users of EDM. Examples include statistical and visualization tools that analyzes social networks and their influence on learning outcomes and productivity.
Courses
In October 2013, Coursera offered a free online course on "Big Data in Education" that taught how and when to use key methods for EDM. This course moved to edX in the summer of 2015, and has continued to run on edX annually since then. A course archive is now available online.
Considerable
amounts of EDM work are published at the peer-reviewed International
Conference on Educational Data Mining, organized by the International Educational Data Mining Society.
In 2011, Chapman & Hall/CRC Press, Taylor and Francis Group
published the first Handbook of Educational Data Mining. This resource
was created for those that are interested in participating in the
educational data mining community.
Along
with technological advancements are costs and challenges associated
with implementing EDM applications. These include the costs to store
logged data and the cost associated with hiring staff dedicated to
managing data systems.
Moreover, data systems may not always integrate seamlessly with one
another and even with the support of statistical and visualization
tools, creating one simplified version of the data can be difficult. Furthermore, choosing which data to mine and analyze can also be challenging,
making the initial stages very time consuming and labor-intensive. From
beginning to end, the EDM strategy and implementation requires one to
uphold privacy and ethics for all stakeholders involved.
Criticisms
Generalizability
– Research in EDM may be specific to the particular educational setting
and time in which the research was conducted, and as such, may not be
generalizable to other institutions. Research also indicates that the
field of educational data mining is concentrated in North America and western cultures and subsequently, other countries and cultures may not be represented in the research and findings. Development of future models should consider applications across multiple contexts.
Privacy
– Individual privacy is a continued concern for the application of data
mining tools. With free, accessible and user-friendly tools in the
market, students and their families may be at risk from the information
that learners provide to the learning system, in hopes to receive
feedback that will benefit their future performance. As users become
savvy in their understanding of online privacy, administrators
of educational data mining tools need to be proactive in protecting the
privacy of their users and be transparent about how and with whom the
information will be used and shared. Development of EDM tools should
consider protecting individual privacy while still advancing the
research in this field.
Plagiarism
– Plagiarism detection is an ongoing challenge for educators and
faculty whether in the classroom or online. However, due to the
complexities associated with detecting and preventing digital plagiarism
in particular, educational data mining tools are not currently
sophisticated enough to accurately address this issue. Thus, the
development of predictive capability in plagiarism-related issues should
be an area of focus in future research.
Adoption – It is unknown how widespread the adoption of EDM
is and the extent to which institutions have applied and considered
implementing an EDM strategy. As such, it is unclear whether there are
any barriers that prevent users from adopting EDM in their educational
settings.
An intelligent tutoring system (ITS) is a computer system that aims to provide immediate and customized instruction or feedback to learners, usually without requiring intervention from a human teacher.
ITSs have the common goal of enabling learning in a meaningful and
effective manner by using a variety of computing technologies. There
are many examples of ITSs being used in both formal education and
professional settings in which they have demonstrated their capabilities
and limitations. There is a close relationship between intelligent
tutoring, cognitive learning theories and design; and there is ongoing
research to improve the effectiveness of ITS. An ITS typically aims to
replicate the demonstrated benefits of one-to-one, personalized
tutoring, in contexts where students would otherwise have access to
one-to-many instruction from a single teacher (e.g., classroom
lectures), or no teacher at all (e.g., online homework). ITSs are often designed with the goal of providing access to high quality education to each and every student.
History
Early mechanical systems
Skinner teaching machine 08
The possibility of intelligent machines have been discussed for centuries. Blaise Pascal created the first calculating machine capable of mathematical functions in the 17th century simply called Pascal's Calculator. At this time the mathematician and philosopher Gottfried Wilhelm Leibniz envisioned machines capable of reasoning and applying rules of logic to settle disputes (Buchanan, 2006). These early works contributed to the development of the computer and future applications.
The concept of intelligent machines for instructional use date back as early as 1924, when Sidney Pressey of Ohio State University created a mechanical teaching machine to instruct students without a human teacher.
His machine resembled closely a typewriter with several keys and a
window that provided the learner with questions. The Pressey Machine
allowed user input and provided immediate feedback by recording their
score on a counter.
Pressey himself was influenced by Edward L. Thorndike,
a learning theorist and educational psychologist at the Columbia
University Teacher College of the late 19th and early 20th centuries.
Thorndike posited laws for maximizing learning. Thorndike's laws
included the law of effect, the law of exercise, and the law of recency.
Following later standards, Pressey's teaching and testing machine would
not be considered intelligent as it was mechanically run and was based
on one question and answer at a time, but it set an early precedent for future projects.
By the 1950s and 1960s, new perspectives on learning were emerging. Burrhus Frederic "B.F." Skinner at Harvard University did not agree with Thorndike's learning theory of connectionism or Pressey's teaching machine. Rather, Skinner was a behaviourist who believed that learners should construct their answers and not rely on recognition.
He too, constructed a teaching machine structured using an incremental
mechanical system that would reward students for correct responses to
questions.
Early electronic systems
In
the period following the second world war, mechanical binary systems
gave way to binary based electronic machines. These machines were
considered intelligent when compared to their mechanical counterparts as
they had the capacity to make logical decisions. However, the study of
defining and recognizing a machine intelligence was still in its
infancy.
Alan Turing,
a mathematician, logician and computer scientist, linked computing
systems to thinking. One of his most notable papers outlined a
hypothetical test to assess the intelligence of a machine which came to
be known as the Turing test.
Essentially, the test would have a person communicate with two other
agents, a human and a computer asking questions to both recipients. The
computer passes the test if it can respond in such a way that the human
posing the questions cannot differentiate between the other human and
the computer. The Turing test has been used in its essence for more
than two decades as a model for current ITS development. The main ideal
for ITS systems is to effectively communicate.
As early as the 1950s programs were emerging displaying intelligent
features. Turing's work as well as later projects by researchers such as
Allen Newell, Clifford Shaw, and Herb Simon showed programs capable of
creating logical proofs and theorems. Their program, The Logic Theorist
exhibited complex symbol manipulation and even generation of new
information without direct human control and is considered by some to be
the first AI program. Such breakthroughs would inspire the new field of
Artificial Intelligence officially named in 1956 by John McCarthy in 1956 at the Dartmouth Conference. This conference was the first of its kind that was devoted to scientists and research in the field of AI.
The PLATO V CAI terminal in 1981
The latter part of the 1960s and 1970s saw many new CAI
(Computer-Assisted instruction) projects that built on advances in
computer science. The creation of the ALGOL
programming language in 1958 enabled many schools and universities to
begin developing Computer Assisted Instruction (CAI) programs. Major
computer vendors and federal agencies in the US such as IBM, HP, and the
National Science Foundation funded the development of these projects.
Early implementations in education focused on programmed instruction
(PI), a structure based on a computerized input-output system. Although
many supported this form of instruction, there was limited evidence
supporting its effectiveness. The programming language LOGO was created in 1967 by Wally Feurzeig, Cynthia Solomon, and Seymour Papert
as a language streamlined for education. PLATO, an educational terminal
featuring displays, animations, and touch controls that could store and
deliver large amounts of course material, was developed by Donald
Bitzer in the University of Illinois in the early 1970s. Along with
these, many other CAI projects were initiated in many countries
including the US, the UK, and Canada.
At the same time that CAI was gaining interest, Jaime Carbonell
suggested that computers could act as a teacher rather than just a tool
(Carbonell, 1970). A new perspective would emerge that focused on the
use of computers to intelligently coach students called Intelligent
Computer Assisted Instruction or Intelligent Tutoring Systems (ITS).
Where CAI used a behaviourist perspective on learning based on Skinner's
theories (Dede & Swigger, 1988), ITS drew from work in cognitive psychology, computer science, and especially artificial intelligence.
There was a shift in AI research at this time as systems moved from the
logic focus of the previous decade to knowledge based systems—systems
could make intelligent decisions based on prior knowledge (Buchanan,
2006). Such a program was created by Seymour Papert and Ira Goldstein who created Dendral,
a system that predicted possible chemical structures from existing
data. Further work began to showcase analogical reasoning and language
processing. These changes with a focus on knowledge had big implications
for how computers could be used in instruction. The technical
requirements of ITS, however, proved to be higher and more complex than
CAI systems and ITS systems would find limited success at this time.
Towards the latter part of the 1970s interest in CAI technologies began to wane.
Computers were still expensive and not as available as expected.
Developers and instructors were reacting negatively to the high cost of
developing CAI programs, the inadequate provision for instructor
training, and the lack of resources.
Microcomputers and intelligent systems
The
microcomputer revolution in the late 1970s and early 1980s helped to
revive CAI development and jumpstart development of ITS systems.
Personal computers such as the Apple 2, Commodore PET, and TRS-80
reduced the resources required to own computers and by 1981, 50% of US
schools were using computers (Chambers & Sprecher, 1983).
Several CAI projects utilized the Apple 2 as a system to deliver CAI
programs in high schools and universities including the British Columbia
Project and California State University Project in 1981.
The early 1980s would also see Intelligent Computer-Assisted
Instruction (ICAI) and ITS goals diverge from their roots in CAI. As CAI
became increasingly focused on deeper interactions with content created
for a specific area of interest, ITS sought to create systems that
focused on knowledge of the task and the ability to generalize that
knowledge in non-specific ways (Larkin & Chabay, 1992).
The key goals set out for ITS were to be able to teach a task as well
as perform it, adapting dynamically to its situation. In the transition
from CAI to ICAI systems, the computer would have to distinguish not
only between the correct and incorrect response but the type of
incorrect response to adjust the type of instruction. Research in Artificial Intelligence and Cognitive Psychology
fueled the new principles of ITS. Psychologists considered how a
computer could solve problems and perform 'intelligent' activities. An
ITS programme would have to be able to represent, store and retrieve
knowledge and even search its own database to derive its own new
knowledge to respond to learner's questions. Basically, early
specifications for ITS or (ICAI) require it to "diagnose errors and
tailor remediation based on the diagnosis" (Shute & Psotka, 1994,
p. 9). The idea of diagnosis and remediation is still in use today when programming ITS.
A key breakthrough in ITS research was the creation of LISPITS, a
program that implemented ITS principles in a practical way and showed
promising effects increasing student performance. LISPITS was developed
and researched in 1983 as an ITS system for teaching students the LISP
programming language (Corbett & Anderson, 1992).
LISPITS could identify mistakes and provide constructive feedback to
students while they were performing the exercise. The system was found
to decrease the time required to complete the exercises while improving
student test scores (Corbett & Anderson, 1992). Other ITS systems beginning to develop around this time include TUTOR created by Logica in 1984 as a general instructional tool and PARNASSUS created in Carnegie Mellon University in 1989 for language instruction.
Modern ITS
After
the implementation of initial ITS, more researchers created a number of
ITS for different students. In the late 20th century, Intelligent
Tutoring Tools (ITTs) was developed by the Byzantium project, which
involved six universities. The ITTs were general purpose tutoring system
builders and many institutions had positive feedback while using them.
(Kinshuk, 1996)
This builder, ITT, would produce an Intelligent Tutoring Applet (ITA)
for different subject areas. Different teachers created the ITAs and
built up a large inventory of knowledge that was accessible by others
through the Internet. Once an ITS was created, teachers could copy it
and modify it for future use. This system was efficient and flexible.
However, Kinshuk and Patel believed that the ITS was not designed from
an educational point of view and was not developed based on the actual
needs of students and teachers (Kinshuk and Patel, 1997). Recent work has employed ethnographic and design research methods to examine the ways ITSs are actually used by students and teachers across a range of contexts, often revealing unanticipated needs that they meet, fail to meet, or in some cases, even create.
Modern day ITSs typically try to replicate the role of a teacher
or a teaching assistant, and increasingly automate pedagogical functions
such as problem generation, problem selection, and feedback generation.
However, given a current shift towards blended learning models, recent
work on ITSs has begun focusing on ways these systems can effectively
leverage the complementary strengths of human-led instruction from a
teacher or peer, when used in co-located classrooms or other social contexts.
There were three ITS projects that functioned based on conversational dialogue: AutoTutor, Atlas (Freedman, 1999),
and Why2. The idea behind these projects was that since students learn
best by constructing knowledge themselves, the programs would begin with
leading questions for the students and would give out answers as a last
resort. AutoTutor's students focused on answering questions about
computer technology, Atlas's students focused on solving quantitative
problems, and Why2's students focused on explaining physical systems
qualitatively. (Graesser, VanLehn, and others, 2001) Other similar tutoring systems such as Andes (Gertner, Conati, and VanLehn, 1998)
tend to provide hints and immediate feedback for students when students
have trouble answering the questions. They could guess their answers
and have correct answers without deep understanding of the concepts.
Research was done with a small group of students using Atlas and Andes
respectively. The results showed that students using Atlas made
significant improvements compared with students who used Andes.
However, since the above systems require analysis of students'
dialogues, improvement is yet to be made so that more complicated
dialogues can be managed.
Structure
Intelligent
tutoring systems (ITSs) consist of four basic components based on a
general consensus amongst researchers (Nwana,1990; Freedman, 2000; Nkambou et al., 2010):
The Domain model
The Student model
The Tutoring model, and
The User interface model
The domain model (also known as the cognitive model or expert knowledge model) is built on a theory of learning, such as the ACT-R
theory which tries to take into account all the possible steps required
to solve a problem. More specifically, this model "contains the
concepts, rules, and problem-solving strategies of the domain to be
learned. It can fulfill several roles: as a source of expert knowledge, a
standard for evaluating the student's performance or for detecting
errors, etc." (Nkambou et al., 2010, p. 4). Another approach for developing domain models is based on Stellan Ohlsson's Theory of Learning from performance errors, known as constraint-based modelling (CBM). In this case, the domain model is presented as a set of constraints on correct solutions.
The student model can be thought of as an overlay on the
domain model. It is considered as the core component of an ITS paying
special attention to student's cognitive and affective states and their
evolution as the learning process advances. As the student works
step-by-step through their problem solving process, an ITS engages in a
process called model tracing. Anytime the student model deviates from the domain model, the system identifies, or flags,
that an error has occurred. On the other hand, in constraint-based
tutors the student model is represented as an overlay on the constraint
set. Constraint-based tutors
evaluate the student's solution against the constraint set, and
identify satisfied and violated constraints. if there are any violated
constraints, the student's solution is incorrect, and the ITS provides
feedback on those constraints. Constraint-based tutors provide negative feedback (i.e. feedback on errors) and also positive feedback.
The tutor model accepts information from the domain and
student models and makes choices about tutoring strategies and actions.
At any point in the problem-solving process the learner may request
guidance on what to do next, relative to their current location in the
model. In addition, the system recognizes when the learner has deviated
from the production rules of the model and provides timely feedback for
the learner, resulting in a shorter period of time to reach proficiency
with the targeted skills. The tutor model may contain several hundred production rules that can be said to exist in one of two states, learned or unlearned.
Every time a student successfully applies a rule to a problem, the
system updates a probability estimate that the student has learned the
rule. The system continues to drill students on exercises that require
effective application of a rule until the probability that the rule has
been learned reaches at least 95% probability.
Knowledge tracing tracks the learner's progress from
problem to problem and builds a profile of strengths and weaknesses
relative to the production rules. The cognitive tutoring system
developed by John Anderson at Carnegie Mellon University presents information from knowledge tracing as a skillometer,
a visual graph of the learner's success in each of the monitored skills
related to solving algebra problems. When a learner requests a hint, or
an error is flagged, the knowledge tracing data and the skillometer are
updated in real-time.
The user interface component "integrates three types of
information that are needed in carrying out a dialogue: knowledge about
patterns of interpretation (to understand a speaker) and action (to
generate utterances) within dialogues; domain knowledge needed for
communicating content; and knowledge needed for communicating intent"
(Padayachee, 2002, p. 3).
Nkambou et al. (2010) make mention of Nwana's (1990)
review of different architectures underlining a strong link between
architecture and paradigm (or philosophy). Nwana (1990) declares, "[I]t
is almost a rarity to find two ITSs based on the same architecture
[which] results from the experimental nature of the work in the area"
(p. 258). He further explains that differing tutoring philosophies
emphasize different components of the learning process (i.e., domain,
student or tutor). The architectural design of an ITS reflects this
emphasis, and this leads to a variety of architectures, none of which,
individually, can support all tutoring strategies (Nwana, 1990, as cited
in Nkambou et al., 2010). Moreover, ITS projects may vary according to
the relative level of intelligence of the components. As an example, a
project highlighting intelligence in the domain model may generate
solutions to complex and novel problems so
that students can always have new problems to work on, but it might only
have simple methods for teaching those problems, while a system that
concentrates on multiple or novel ways of teaching a particular topic
might find a less sophisticated representation of that content
sufficient.
Design and development methods
Apart
from the discrepancy amongst ITS architectures each emphasizing
different elements, the development of an ITS is much the same as any instructional design
process. Corbett et al. (1997) summarized ITS design and development as
consisting of four iterative stages: (1) needs assessment, (2)
cognitive task analysis, (3) initial tutor implementation and (4)
evaluation.
The first stage known as needs assessment is common to any
instructional design process, especially software development. This
involves a learner analysis, consultation with subject matter
experts and/or the instructor(s). This first step is part of the
development of the expert/knowledge and student domain. The goal is to
specify learning goals and to outline a general plan for the curriculum;
it is imperative not to computerize traditional concepts but develop a
new curriculum structure by defining the task in general and
understanding learners' possible behaviours dealing with the task and to
a lesser degree the tutor's behavior. In doing so, three crucial
dimensions need to be dealt with: (1) the probability a student is able
to solve problems; (2) the time it takes to reach this performance level
and (3) the probability the student will actively use this knowledge in
the future. Another important aspect that requires analysis is cost
effectiveness of the interface. Moreover, teachers and student entry
characteristics such as prior knowledge must be assessed since both
groups are going to be system users.
The second stage, cognitive task analysis, is a detailed approach
to expert systems programming with the goal of developing a valid
computational model of the required problem solving knowledge. Chief
methods for developing a domain model include: (1) interviewing domain
experts, (2) conducting "think aloud" protocol studies with domain
experts, (3) conducting "think aloud" studies with novices and (4)
observation of teaching and learning behavior. Although the first method
is most commonly used, experts are usually incapable of reporting
cognitive components. The "think aloud" methods, in which the experts is
asked to report aloud what s/he is thinking when solving typical
problems, can avoid this problem.
Observation of actual online interactions between tutors and students
provides information related to the processes used in problem-solving,
which is useful for building dialogue or interactivity into tutoring
systems.
The third stage, initial tutor implementation, involves setting
up a problem solving environment to enable and support an authentic
learning process. This stage is followed by a series of evaluation
activities as the final stage which is again similar to any software
development project.
The fourth stage, evaluation includes (1) pilot studies to
confirm basic usability and educational impact; (2) formative
evaluations of the system under development, including (3) parametric
studies that examine the effectiveness of system features and finally,
(4) summative evaluations of the final tutor's effect: learning rate and
asymptotic achievement levels.
Eight principles of ITS design and development
Anderson et al. (1987) outlined eight principles for intelligent tutor design and Corbett et al. (1997)
later elaborated on those principles highlighting an all-embracing
principle which they believed governed intelligent tutor design, they
referred to this principle as:
Principle 0: An intelligent tutor system should enable the student to work to the successful conclusion of problem solving.
Represent student competence as a production set.
Communicate the goal structure underlying the problem solving.
Provide instruction in the problem solving context.
Promote an abstract understanding of the problem-solving knowledge.
Minimize working memory load.
Provide immediate feedback on errors.
Adjust the grain size of instruction with learning.
Facilitate successive approximations to the target skill.
Use in practice
All this is a substantial amount of work, even if authoring tools have become available to ease the task.
This means that building an ITS is an option only in situations in
which they, in spite of their relatively high development costs, still
reduce the overall costs through reducing the need for human instructors
or sufficiently boosting overall productivity. Such situations occur
when large groups need to be tutored simultaneously or many replicated
tutoring efforts are needed. Cases in point are technical training
situations such as training of military recruits and high school
mathematics. One specific type of intelligent tutoring system, the Cognitive Tutor,
has been incorporated into mathematics curricula in a substantial
number of United States high schools, producing improved student
learning outcomes on final exams and standardized tests.
Intelligent tutoring systems have been constructed to help students
learn geography, circuits, medical diagnosis, computer programming,
mathematics, physics, genetics, chemistry, etc. Intelligent Language
Tutoring Systems (ILTS), e.g. this
one, teach natural language to first or second language learners. ILTS
requires specialized natural language processing tools such as large
dictionaries and morphological and grammatical analyzers with acceptable
coverage.
Applications
During the rapid expansion of the web boom, new computer-aided instruction paradigms, such as e-learning and distributed learning, provided an excellent platform for ITS ideas. Areas that have used ITS include natural language processing, machine learning, planning, multi-agent systems, ontologies, semantic Web, and social and emotional computing. In addition, other technologies such as multimedia, object-oriented systems,
modeling, simulation, and statistics have also been connected to or
combined with ITS. Historically non-technological areas such as the
educational sciences and psychology have also been influenced by the
success of ITS.
In recent years, ITS has begun to move away from the search-based to include a range of practical applications.
ITS have expanded across many critical and complex cognitive domains,
and the results have been far reaching. ITS systems have cemented a
place within formal education and these systems have found homes in the
sphere of corporate training and organizational learning. ITS offers
learners several affordances such as individualized learning, just in
time feedback, and flexibility in time and space.
While Intelligent tutoring systems evolved from research in
cognitive psychology and artificial intelligence, there are now many
applications found in education and in organizations. Intelligent
tutoring systems can be found in online environments or in a traditional
classroom computer lab, and are used in K-12 classrooms as well as in
universities. There are a number of programs that target mathematics but
applications can be found in health sciences, language acquisition, and
other areas of formalized learning.
Reports of improvement in student comprehension, engagement,
attitude, motivation, and academic results have all contributed to the
ongoing interest in the investment in and research of theses systems.
The personalized nature of the intelligent tutoring systems affords
educators the opportunity to create individualized programs. Within
education there are a plethora of intelligent tutoring systems, an
exhaustive list does not exist but several of the more influential
programs are listed below.
Education
Algebra Tutor
PAT (PUMP Algebra Tutor or Practical Algebra Tutor) developed by the Pittsburgh Advanced Cognitive Tutor Center at Carnegie Mellon University,
engages students in anchored learning problems and uses modern
algebraic tools in order to engage students in problem solving and in
sharing of their results. The aim of PAT is to tap into a students'
prior knowledge and everyday experiences with mathematics in order to
promote growth. The success of PAT is well documented (ex. Miami-Dade
County Public Schools Office of Evaluation and Research) from both a
statistical (student results) and emotional (student and instructor
feedback) perspective.
SQL-Tutor is the first ever constraint-based tutor developed by the Intelligent Computer Tutoring Group (ICTG) at the University of Canterbury, New Zealand. SQL-Tutor teaches students how to retrieve data from databases using the SQL SELECT statement.
EER-Tutor
is a constraint-based tutor (developed by ICTG) that teaches conceptual
database design using the Entity Relationship model. An earlier version
of EER-Tutor was KERMIT, a stand-alone tutor for ER modelling, whjich
was shown to results in significant improvement of student's knowledge
after one hour of learning (with the effect size of 0.6).
COLLECT-UML
is a constraint-based tutor that supports pairs of students working
collaboratively on UML class diagrams. The tutor provides feedback on
the domain level as well as on collaboration.
Mathematics Tutor
The Mathematics Tutor (Beal, Beck & Woolf, 1998) helps students
solve word problems using fractions, decimals and percentages. The
tutor records the success rates while a student is working on problems
while providing subsequent, lever-appropriate problems for the student
to work on. The subsequent problems that are selected are based on
student ability and a desirable time in is estimated in which the
student is to solve the problem.
eTeacher
eTeacher (Schiaffino et al., 2008) is an intelligent agent or pedagogical agent,
that supports personalized e-learning assistance. It builds student
profiles while observing student performance in online courses.
eTeacher then uses the information from the student's performance to
suggest a personalized courses of action designed to assist their
learning process.
ZOSMAT
ZOSMAT was designed to address all the needs of a real classroom. It
follows and guides a student in different stages of their learning
process. This is a student-centered ITS does this by recording the
progress in a student's learning and the student program changes based
on the student's effort. ZOSMAT can be used for either individual
learning or in a real classroom environment alongside the guidance of a
human tutor.
REALP
REALP was designed to help students enhance their reading comprehension
by providing reader-specific lexical practice and offering personalized
practice with useful, authentic reading materials gathered from the Web.
The system automatically build a user model according to student's
performance. After reading, the student is given a series of exercises
based on the target vocabulary found in reading.
CIRCSlM-Tutor
CIRCSIM_Tutor is an intelligent tutoring system that is used with first
year medical students at the Illinois Institute of Technology. It uses
natural dialogue based, Socratic language to help students learn about
regulating blood pressure.
Why2-Atlas
Why2-Atlas is an ITS that analyses students explanations of physics
principles. The students input their work in paragraph form and the
program converts their words into a proof by making assumptions of
student beliefs that are based on their explanations. In doing this,
misconceptions and incomplete explanations are highlighted. The system
then addresses these issues through a dialogue with the student and asks
the student to correct their essay. A number of iterations may take
place before the process is complete.
SmartTutor
The University of Hong Kong (HKU) developed a SmartTutor to support the
needs of continuing education students. Personalized learning was
identified as a key need within adult education at HKU and SmartTutor
aims to fill that need. SmartTutor provides support for students by
combining Internet technology, educational research and artificial
intelligence.
AutoTutorAutoTutor
assists college students in learning about computer hardware, operating
systems and the Internet in an introductory computer literacy course by
simulating the discourse patterns and pedagogical strategies of a human
tutor. AutoTutor attempts to understand learner's input from the
keyboard and then formulate dialog moves with feedback, prompts,
correction and hints.
ActiveMath
ActiveMath is a web-based, adaptive learning environment for
mathematics. This system strives for improving long-distance learning,
for complementing traditional classroom teaching, and for supporting
individual and lifelong learning.
ESC101-ITS
The Indian Institute of Technology, Kanpur, India developed the
ESC101-ITS, an intelligent tutoring system for introductory programming
problems.
Corporate training and industry
Generalized Intelligent Framework for Tutoring (GIFT) is an educational software designed for creation of computer-based tutoring systems. Developed by the U.S. Army Research Laboratory from 2009 to 2011, GIFT was released for commercial use in May 2012.
GIFT is open-source and domain independent, and can be downloaded
online for free. The software allows an instructor to design a tutoring
program that can cover various disciplines through adjustments to
existing courses. It includes coursework tools intended for use by
researchers, instructional designers, instructors, and students. GIFT is compatible with other teaching materials, such as PowerPoint presentations, which can be integrated into the program.
SHERLOCK
"SHERLOCK" is used to train Air Force technicians to diagnose problems
in the electrical systems of F-15 jets. The ITS creates faulty
schematic diagrams of systems for the trainee to locate and diagnose.
The ITS provides diagnostic readings allowing the trainee to decide
whether the fault lies in the circuit being tested or if it lies
elsewhere in the system. Feedback and guidance are provided by the
system and help is available if requested.
Cardiac Tutor
The Cardiac Tutor's aim is to support advanced cardiac support
techniques to medical personnel. The tutor presents cardiac problems
and, using a variety of steps, students must select various
interventions. Cardiac Tutor provides clues, verbal advice, and feedback
in order to personalize and optimize the learning. Each simulation,
regardless of whether the students were successfully able to help their
patients, results in a detailed report which students then review.
CODES
Cooperative Music Prototype Design is a Web-based environment for
cooperative music prototyping. It was designed to support users,
especially those who are not specialists in music, in creating musical
pieces in a prototyping manner. The musical examples (prototypes) can be
repeatedly tested, played and modified. One of the main aspects of
CODES is interaction and cooperation between the music creators and
their partners.
Effectiveness
Assessing
the effectiveness of ITS programs is problematic. ITS vary greatly in
design, implementation, and educational focus. When ITS are used in a
classroom, the system is not only used by students, but by teachers as
well. This usage can create barriers to effective evaluation for a
number of reasons; most notably due to teacher intervention in student
learning.
Teachers often have the ability to enter new problems into the
system or adjust the curriculum. In addition, teachers and peers often
interact with students while they learn with ITSs (e.g., during an
individual computer lab session or during classroom lectures falling in
between lab sessions) in ways that may influence their learning with the
software.
Prior work suggests that the vast majority of students' help-seeking
behavior in classrooms using ITSs may occur entirely outside of the
software - meaning that the nature and quality of peer and teacher
feedback in a given class may be an important mediator of student
learning in these contexts. In addition, aspects of classroom climate, such as students' overall level of comfort in publicly asking for help, or the degree to which a teacher is physically active in monitoring individual students may add additional sources of variation across evaluation contexts. All of these variables make evaluation of an ITS complex, and may help explain variation in results across evaluation studies.
Despite the inherent complexities, numerous studies have
attempted to measure the overall effectiveness of ITS, often by
comparisons of ITS to human tutors. Reviews of early ITS systems (1995) showed an effect size of d = 1.0 in comparison to no tutoring, where as human tutors were given an effect size of d = 2.0.
Kurt VanLehn's much more recent overview (2011) of modern ITS found
that there was no statistical difference in effect size between expert
one-on-one human tutors and step-based ITS.
Some individual ITS have been evaluated more positively than others.
Studies of the Algebra Cognitive Tutor found that the ITS students
outperformed students taught by a classroom teacher on standardized test
problems and real-world problem solving tasks.
Subsequent studies found that these results were particularly
pronounced in students from special education, non-native English, and
low-income backgrounds.
A more recent meta-analysis suggests that ITSs can exceed the
effectiveness of both CAI and human tutors, especially when measured by
local (specific) tests as opposed to standardized tests. "Students who
received intelligent tutoring outperformed students from conventional
classes in 46 (or 92%) of the 50 controlled evaluations, and the
improvement in performance was great enough to be considered of
substantive importance in 39 (or 78%) of the 50 studies. The median ES
in the 50 studies was 0.66, which is considered a moderate-to-large
effect for studies in the social sciences. It is roughly equivalent to
an improvement in test performance from the 50th to the 75th percentile.
This is stronger than typical effects from other forms of tutoring.
C.-L. C. Kulik and Kulik’s (1991) meta-analysis, for example, found an
average ES of 0.31 in 165 studies of CAI tutoring. ITS gains are about
twice as high. The ITS effect is also greater than typical effects from
human tutoring. As we have seen, programs of human tutoring typically
raise student test scores about 0.4 standard deviations over control
levels. Developers of ITSs long ago set out to improve on the success of
CAI tutoring and to match the success of human tutoring. Our results
suggest that ITS developers have already met both of these goals....
Although effects were moderate to strong in evaluations that measured
outcomes on locally developed tests, they were much smaller in
evaluations that measured outcomes on standardized tests. Average ES on
studies with local tests was 0.73; average ES on studies with
standardized tests was 0.13. This discrepancy is not unusual for
meta-analyses that include both local and standardized tests... local
tests are likely to align well with the objectives of specific
instructional programs. Off-the-shelf standardized tests provide a
looser fit. ... Our own belief is that both local and standardized tests
provide important information about instructional effectiveness, and
when possible, both types of tests should be included in evaluation
studies."
Some recognized strengths of ITS are their ability to provide
immediate yes/no feedback, individual task selection, on-demand hints,
and support mastery learning.
Limitations
Intelligent
tutoring systems are expensive both to develop and implement. The
research phase paves the way for the development of systems that are
commercially viable. However, the research phase is often expensive; it
requires the cooperation and input of subject matter experts, the
cooperation and support of individuals across both organizations and
organizational levels. Another limitation in the development phase is
the conceptualization and the development of software within both budget
and time constraints. There are also factors that limit the
incorporation of intelligent tutors into the real world, including the
long timeframe required for development and the high cost of the
creation of the system components. A high portion of that cost is a
result of content component building.
For instance, surveys revealed that encoding an hour of online
instruction time took 300 hours of development time for tutoring
content. Similarly, building the Cognitive Tutor took a ratio of development time to instruction time of at least 200:1 hours. The high cost of development often eclipses replicating the efforts for real world application.
Intelligent tutoring systems are not, in general, commercially feasible for real-world applications.
A criticism of Intelligent Tutoring Systems currently in use, is
the pedagogy of immediate feedback and hint sequences that are built in
to make the system "intelligent". This pedagogy is criticized for its
failure to develop deep learning in students. When students are given
control over the ability to receive hints, the learning response created
is negative. Some students immediately turn to the hints before
attempting to solve the problem or complete the task. When it is
possible to do so, some students bottom out the hints – receiving as
many hints as possible as fast as possible – in order to complete the
task faster. If students fail to reflect on the tutoring system's
feedback or hints, and instead increase guessing until positive
feedback is garnered, the student is, in effect, learning to do the
right thing for the wrong reasons. Most tutoring systems are currently
unable to detect shallow learning, or to distinguish between productive
versus unproductive struggle (though see, e.g.,). For these and many other reasons (e.g., overfitting of underlying models to particular user populations), the effectiveness of these systems may differ significantly across users.
Another criticism of intelligent tutoring systems is the failure
of the system to ask questions of the students to explain their actions.
If the student is not learning the domain language than it becomes more
difficult to gain a deeper understanding, to work collaboratively in
groups, and to transfer the domain language to writing. For example, if
the student is not "talking science" than it is argued that they are not
being immersed in the culture of science, making it difficult to
undertake scientific writing or participate in collaborative team
efforts. Intelligent tutoring systems have been criticized for being
too "instructivist" and removing intrinsic motivation, social learning
contexts, and context realism from learning.
Practical concerns, in terms of the inclination of the
sponsors/authorities and the users to adapt intelligent tutoring
systems, should be taken into account. First, someone must have a willingness to implement the ITS.
Additionally an authority must recognize the necessity to integrate an
intelligent tutoring software into current curriculum and finally, the
sponsor or authority must offer the needed support through the stages of
the system development until it is completed and implemented.
Evaluation of an intelligent tutoring system is an important phase; however, it is often difficult, costly, and time consuming.
Even though there are various evaluation techniques presented in the
literature, there are no guiding principles for the selection of
appropriate evaluation method(s) to be used in a particular context.
Careful inspection should be undertaken to ensure that a complex system
does what it claims to do. This assessment may occur during the design
and early development of the system to identify problems and to guide
modifications (i.e. formative evaluation).
In contrast, the evaluation may occur after the completion of the
system to support formal claims about the construction, behaviour of, or
outcomes associated with a completed system (i.e. summative
evaluation).
The great challenge introduced by the lack of evaluation standards
resulted in neglecting the evaluation stage in several existing ITS'.
Improvements
Intelligent
tutoring systems are less capable than human tutors in the areas of
dialogue and feedback. For example, human tutors are able to interpret
the affective state of the student, and potentially adapt instruction in
response to these perceptions. Recent work is exploring potential
strategies for overcoming these limitations of ITSs, to make them more
effective.
Dialogue
Human tutors have the ability to understand a person's tone and
inflection within a dialogue and interpret this to provide continual
feedback through an ongoing dialogue. Intelligent tutoring systems are
now being developed to attempt to simulate natural conversations. To
get the full experience of dialogue there are many different areas in
which a computer must be programmed; including being able to understand
tone, inflection, body language, and facial expression and then to
respond to these. Dialogue in an ITS can be used to ask specific
questions to help guide students and elicit information while allowing
students to construct their own knowledge.
The development of more sophisticated dialogue within an ITS has been a
focus in some current research partially to address the limitations and
create a more constructivist approach to ITS.
In addition, some current research has focused on modeling the nature
and effects of various social cues commonly employed within a dialogue
by human tutors and tutees, in order to build trust and rapport (which
have been shown to have positive impacts on student learning).
Emotional affect
A growing body of work is considering the role of affect
on learning, with the objective of developing intelligent tutoring
systems that can interpret and adapt to the different emotional states.
Humans do not just use cognitive processes in learning but the
affective processes they go through also plays an important role. For
example, learners learn better when they have a certain level of
disequilibrium (frustration), but not enough to make the learner feel
completely overwhelmed.
This has motivated affective computing to begin to produce and research
creating intelligent tutoring systems that can interpret the affective
process of an individual.
An ITS can be developed to read an individual's expressions and other
signs of affect in an attempt to find and tutor to the optimal affective
state for learning. There are many complications in doing this since
affect is not expressed in just one way but in multiple ways so that for
an ITS to be effective in interpreting affective states it may require a
multimodal approach (tone, facial expression, etc...). These ideas have created a new field within ITS, that of Affective Tutoring Systems (ATS).
One example of an ITS that addresses affect is Gaze Tutor which was
developed to track students eye movements and determine whether they are
bored or distracted and then the system attempts to reengage the
student.
Rapport Building
To date, most ITSs have focused purely on the cognitive aspects
of tutoring and not on the social relationship between the tutoring
system and the student. As demonstrated by the Computers are social actors paradigm humans often project social heuristics onto computers. For example in observations of young children interacting with Sam the CastleMate,
a collaborative story telling agent, children interacted with this
simulated child in much the same manner as they would a human child.
It has been suggested that to effectively design an ITS that builds
rapport with students, the ITS should mimic strategies of instructional
immediacy, behaviors which bridge the apparent social distance between
students and teachers such as smiling and addressing students by name.
With regard to teenagers, Ogan et. al draw from observations of close
friends tutoring each other to argue that in order for an ITS to build
rapport as a peer to a student, a more involved process of trust
building is likely necessary which may ultimately require that the
tutoring system possess the capability to effectively respond to and
even produce seemingly rude behavior in order to mediate motivational
and affective student factors through playful joking and taunting.
Teachable Agents
Traditionally ITSs take on the role of autonomous tutors, however
they can also take on the role of tutees for the purpose of learning by
teaching exercises. Evidence suggests that learning by teaching can be
an effective strategy for mediating self-explanation, improving feelings
of self-efficacy, and boosting educational outcomes and retention.
In order to replicate this effect the roles of the student and ITS can
be switched. This can be achieved by designing the ITS to have the
appearance of being taught as is the case in the Teachable Agent
Arithmetic Game and Betty's Brain.
Another approach is to have students teach a machine learning agent
which can learn to solve problems by demonstration and correctness
feedback as is the case in the APLUS system built with SimStudent.
In order to replicate the educational effects of learning by teaching
teachable agents generally have a social agent built on top of them
which poses questions or conveys confusion. For example Betty from
Betty's Brain will prompt the student to ask her questions to make sure
that she understands the material, and Stacy from APLUS will prompt the
user for explanations of the feedback provided by the student.
Cardiac fibrosis commonly refers to the excess deposition of extracellular matrix in the cardiac muscle, but the term may also refer to an abnormal thickening of the heart valves due to inappropriate proliferation of cardiac fibroblasts.
Fibrotic cardiac muscle is stiffer and less compliant and is seen in
the progression to heart failure. The description below focuses on a
specific mechanism of valvular pathology but there are other causes of
valve pathology and fibrosis of the cardiac muscle.
Fibrocyte cells normally secrete collagen,
and function to provide structural support for the heart. When
over-activated this process causes thickening and fibrosis of the valve,
with white tissue building up primarily on the tricuspid valve, but also occurring on the pulmonary valve. The thickening and loss of flexibility eventually may lead to valvular dysfunction and right-sided heart failure.
Connection with excess blood serotonin (5-HT)
Certain diseases such as gastrointestinal carcinoid tumors of the mid-gut, which sometimes release large amounts of 5-hydroxytryptamine, commonly known as 5-HT or serotonin
into the blood, may produce a characteristic pattern of mostly
right-sided cardiac fibrosis which can be identified at autopsy. This
pathology has also been seen in certain East-African tribes who eat
foods (Matoke —a green banana) containing excess amounts of serotonin.
Connection with direct serotonergic agonist drugs
Elevated
prevalence of cardiac fibrosis and related valvopathies was found to be
associated with use of a number of unrelated drugs following long-term
statistical analysis once the drugs had been on the market for some
time. The cause of this was unknown at the time, but eventually it was
realised that all the implicated drugs acted as agonists at 5-HT2B receptors in the heart in addition to their intended sites of action elsewhere in the body.
The precise mechanisms involved remain elusive however, as while the cardiotoxicity shows some dose–response relationship,
it does not always develop, and consistent daily use over an extended
period tends to be most strongly predictive of development of
valvopathy.
The heart valve changes seen with moderate and intermittent use
can result in permanent damage and life-threatening heart problems if
use of the causative drug is increased or continued, however
longitudinal studies of former patients suggest that the damage will
heal over time to some extent at least.
Anorectics
Some appetite suppressant drugs such as fenfluramine (which in combination with phentermine was marketed as Pondimin and commonly referred to as fen-phen), chlorphentermine, and aminorex (along with its analogue 4-Methylaminorex which has seen sporadic use as a recreational drug) induce a similar pattern of cardiac fibrosis (and pulmonary hypertension), apparently by over-stimulating 5HT2B receptors on the cardiac fibroblast cells.
These drugs consequently tend to cause increased risk of heart
valve damage and subsequent heart failure, which eventually led to them
being withdrawn from the market.
Antimigraine drugs
Certain antimigraine drugs which are targeted at serotonin receptors as vasoconstrictive agents, have long been known to be associated with pulmonary hypertension and Raynaud's phenomenon (both vasoconstrictive effects), as well as retroperitoneal fibrosis (a fibrotic cell/fibrocyte proliferation effect, thought to be similar to cardiac valve fibrosis).
These drugs include ergotamine and methysergide and both drugs can also cause cardiac fibrosis.
Antiparkinson drugs
Certain antiparkinson drugs, although targeted at dopaminergic receptors, cross-react with serotoninergic 5-HT2B receptors as well, and have been reported to cause cardiac fibrosis.
These drugs include pergolide and cabergoline.
Antihypertensive drugs
Guanfacine may be a 5-HT2B agonist, based on the results of theoretical modeling and high-throughput screening.
Pergolide
Pergolide
was an antiparkinson medications that was in decreasing use since
reported in 2003 to be associated with cardiac fibrosis.
In March 2007, pergolide was withdrawn from the U.S. market due to
serious valvular damage that was shown in two independent studies.
Cabergoline
Like
pergolide, cabergoline has been linked to cardiac damage. Among similar
antiparkinsonian drugs, cabergoline exhibits the same type of serotonin
receptor binding as pergolide. Although lisuride, a related drug, also binds to the 5-HT2B receptor, it acts as an antagonist rather than as an agonist.
In January 2007, cabergoline (Dostinex) was reported also to be associated with valvular proliferation heart damage.
Recreational drugs
Several serotonergic recreational drugs, including the empathogensMDA and MDMA ("ecstasy"), and some hallucinogens such as DOI and Bromo-DragonFLY, have all been shown to act as 5-HT2B agonists in vitro, but how significant this may be as a risk factor associated with their recreational use is unclear.
The piperazine derivative mCPP (a major metabolite of trazodone) is a 5-HT2B agonist in animal models, but actually behaves as a 5-HT2Bantagonist in humans.
MDMA
One study of
human users of MDMA ("ecstasy") found that they did have heart valve
changes suggestive of early cardiac fibrosis, which were not present in
non-MDMA using controls, suggesting that MDMA use certainly has the potential to cause this kind of heart damage.
On the other hand, there is as yet no statistical evidence to
establish or negate significant increases in rates of cardiac
valvopathies in current or former MDMA users. Absent studies on point,
it may be speculated that as with other 5-HT2B agonists,
development of heart valve damage may be dependent on the frequency and
duration of use and the total cumulative exposure over time. If that is
the case, then the heaviest users are likely to face the greatest risk
of heart damage.
Other serotonergic pharmacologics in question
The SSRI antidepressants raise blood serotonin levels,
and thus may be capable of the same risks, though it is thought that
the risk is substantially lower with such drugs. The amino acid L-tryptophan also raises blood serotonin, and may present the same risk as well; though, again, the risk is considered to be low.
However, the tryptophan derivative 5-HTP (5-hydroxytryptophan), used in the treatment of depression, raises blood serotonin level considerably.
It has yet to be reported to be associated with valve disease or other
fibrosis, but for the previous theoretical reasons, it has been
suggested as a possible danger.
When 5-HTP is used in medicine, it is generally administered along with carbidopa,
which prevents the peripheral decarboxylation of 5-HTP to serotonin and
so ensures that only brain serotonin levels are increased without
producing peripheral side effects, however 5-HTP is also sold without
carbidopa as a dietary supplement, and may have increased risks when
taken by itself without carbidopa.
Possible treatments
The
most obvious treatment for cardiac valve fibrosis or fibrosis in other
locations, consists of stopping the stimulatory drug or production of
serotonin. Surgical tricuspid valve replacement for severe stenosis (blockage of blood flow) has been necessary in some patients.
A compound found in red wine, resveratrol has been found to slow the development of cardiac fibrosis. More sophisticated approaches of countering cardiac fibrosis like microRNA inhibition (miR-21, for example) are being tested in animal models.
Psychophysiology (from Greekψῡχή, psȳkhē, "breath, life, soul"; φύσις, physis, "nature, origin"; and -λογία, -logia) is the branch of psychology that is concerned with the physiological bases of psychological processes.
While psychophysiology was a general broad field of research in the
1960s and 1970s, it has now become quite specialized, and has branched
into subspecializations such as social psychophysiology, cardiovascular
psychophysiology, cognitive psychophysiology, and cognitive neuroscience.
Background
Some people have difficulty distinguishing a psychophysiologist from a physiological psychologist, two very different perspectives. Psychologists are interested in why we may fear spiders and physiologists may be interested in the input/output system of the amygdala.
A psychophysiologist will attempt to link the two. Psychophysiologists
generally study the psychological/physiological link in intact human
subjects. While early psychophysiologists almost always examined the
impact of psychological states on physiological system responses, since
the 1970s, psychophysiologists also frequently study the impact of
physiological states and systems on psychological states and processes.
It is this perspective of studying the interface of mind and body that
makes psychophysiologists most distinct.
Continuing the comparison between a psychophysiologist and a
physiological psychologist, a psychophysiologist may look at how
exposure to a stressful situation will produce a result in the
cardiovascular system such as a change in heart rate (HR),
vasodilation/vasoconstriction, myocardial contractility, or stroke
volume. A physiological psychologist may look at how one cardiovascular
event may influence another cardiovascular or endocrine event, or how
activation of one neural brain structure exerts excitatory activity in
another neural structure which then induces an inhibitory effect in some
other system. Often, physiological psychologists examine the effects
that they study in infrahuman subjects using surgical or invasive
techniques and processes.
Psychophysiology is closely related to the field of neuroscience and social neuroscience, which primarily concerns itself with relationships between psychological events and brain responses. Psychophysiology is also related to the medical discipline known as psychosomatics.
While psychophysiology was a discipline off the mainstream of
psychological and medical science prior to roughly the 1960 and 1970s,
more recently, psychophysiology has found itself positioned at the
intersection of psychological and medical science, and its popularity
and importance have expanded commensurately with the realization of the
inter-relatedness of mind and body.
Measures
Psychophysiology measures exist in three domains; reports, readings, and behavior.
Evaluative reports involve participant introspection and self-ratings
of internal psychological states or physiological sensations, such as
self-report of arousal levels on the self-assessment manikin, or measures of interoceptive visceral awareness such as heartbeat detection.
Merits to self-report are an emphasis on accurately understand the
participants' subjective experience and understanding their perception;
however, its pitfalls include the possibility of participants
misunderstanding a scale or incorrectly recalling events. Physiological responses also can be measured via instruments that read bodily events such as heart rate change, electrodermal activity (EDA),
muscle tension, and cardiac output. Many indices are part of modern
psychophysiology, including brain waves (electroencephalography, EEG), fMRI
(functional magnetic resonance imaging), electrodermal activity (a
standardized term encompassing skin conductance response, SCR, and
galvanic skin response, GSR), cardiovascular measures (heart rate, HR; beats per minute, BPM; heart rate variability, HRV; vasomotor activity), muscle activity (electromyography, EMG), electrogastrogram (EGG) changes in pupil diameter with thought and emotion (pupillometry), eye movements, recorded via the electro-oculogram (EOG) and direction-of-gaze methods, and cardiodynamics, recorded via impedance cardiography. These measures are beneficial because they provide accurate and perceiver-independent objective data recorded by machinery.
The downsides, however, are that any physical activity or motion can
alter responses, and basal levels of arousal and responsiveness can
differ among individuals and even between situations.
Finally, one can measure overt action or behavior, which involves
the observation and recording actual actions, such as running,
freezing, eye movement, and facial expression. These are good response
measures and easy to record in animals, but they are not as frequently
used in human studies.
Uses
Psychophysiological measures are often used to study emotion and attention responses to stimuli, during exertion, and increasingly, to better understand cognitive processes.
Physiological sensors have been used to detect emotions in schools and intelligent tutoring systems.
Emotion
It has long been recognized that emotional episodes are partly constituted by physiological responses.
Early work done linking emotions to psychophysiology started with
research on mapping consistent autonomic nervous system (ANS) responses
to discrete emotional states. For example, anger might be constituted by
a certain set of physiological responses, such as increased cardiac
output and high diastolic blood pressure, which would allow us to better
understand patterns and predict emotional responses. Some studies were
able to detect consistent patterns of ANS responses that corresponded to
specific emotions under certain contexts, like an early study by Paul
Ekman and colleagues in 1983 "Emotion-specific activity in the autonomic
nervous system was generated by constructing facial prototypes of
emotion muscle by muscle and by reliving past emotional experiences. The
autonomic activity produced distinguished not only between positive and
negative emotions, but also among negative emotions".
However, as more studies were conducted, more variability was found in
ANS responses to discrete emotion inductions, not only among individuals
but also over time in the same individuals, and greatly between social
groups.
Some of these differences can be attributed to variables like induction
technique, context of the study, or classification of stimuli, which
can alter a perceived scenario or emotional response. However it was
also found that features of the participant could also alter ANS
responses. Factors such as basal level of arousal at the time of
experimentation or between test recovery, learned or conditioned
responses to certain stimuli, range and maximal level of effect of ANS
action, and individual attentiveness can all alter physiological
responses in a lab setting.
Even supposedly discrete emotional states fail to show specificity. For
example, some emotional typologists consider fear to have subtypes,
which might involve fleeing or freezing, both of which can have distinct
physiological patterns and potentially distinct neural circuitry.
As such no definitive correlation can be drawn linking specific
autonomic patterns to discrete emotions, causing emotion theorists to
rethink classical definitions of emotions.
Psychophysiological inference and physiological computer games
Physiological computing represents a category of affective computing
that incorporates real-time software adaption to the
psychophysiological activity of the user. The main goal of this is to
build a computer that responds to user emotion, cognition and
motivation. The approach is to enable implicit and symmetrical
human-computer communication by granting the software access to a
representation of the user's psychological status.
There are several possible methods to represent the psychological state of the user (discussed in the affective computing
page). The advantages of using psychophysiological indices are that
their changes are continuous, measures are covert and implicit, and only
available data source when the user interacts with the computer without
any explicit communication or input device. These systems rely upon an
assumption that the psychophysiological measure is an accurate
one-to-one representation of a relevant psychological dimension such as
mental effort, task engagement and frustration.
Physiological computing systems all contain an element that may
be termed as an adaptive controller that may be used to represent the
player. This adaptive controller represents the decision-making process
underlying software adaptation. In their simplest form, adaptive
controllers are expressed in Boolean
statements. Adaptive controllers encompass not only the
decision-making rules, but also the psychophysiological inference that
is implicit in the quantification of those trigger points used to
activate the rules. The representation of the player using an adaptive
controller can become very complex and often only one-dimensional. The
loop used to describe this process is known as the biocybernetic loop.
The biocybernetic loop describes the closed loop system that receives
psychophysiological data from the player, transforms that data into a
computerized response, which then shapes the future psychophysiological
response from the player. A positive control loop tends towards
instability as player-software loop strives towards a higher standard of
desirable performance. The physiological computer game may wish to
incorporate both positive and negative loops into the adaptive
controller.
