Adaptive E-learning with Transparent User Identification
Kamen Kanev, Noriaki Kamiya, Nikolay Mirenkov, Go Kanda, Akiyuki Takagi
University of Aizu, Aizu-wakamatsu City, Fukishima-ken, 965-8580, Japan
{kanev|kamiya|nikmir}@u-aizu.ac.jp
Abstract
In this work we discuss methods and technologies
for transparent user authentication in the context
of adaptive e-learning. A new collaborative elearning environment model with support for
adaptive applications is proposed. Based on it an
augmented interactive desktop that allows
individual and shared use of digitally enhanced
educational handouts as tangible interface
components is developed. Cluster Pattern Interface
(CLUSPI) enabled cards for elementary math
tutoring are designed and educational activities at
individual, group, and class levels are considered.
Keywords: E-learning, collaborative learning,
transparent authentication, RFID, fingerprint
recognition, adaptive applications.
1. Introduction
User recognition and authentication play an
important role in e-learning, especially when
adaptive applications are considered. Widely used
password-based authentication, however, appears
to be difficult to employ when quick, transparent
context changes are required. Frequent user
changes that naturally occur when two or more
learners are collaborating and sharing an elearning workstation, for example, can hardly be
handled through password authentication.
Furthermore, in adaptive applications, user context
management and personalization of application
interfaces and functionality have to be conducted
on a continuous basis.
In our previous studies [1] we have
investigated collaborative language learning in a
dynamic group environment where students are
free to work individually, to do pair work, or to
form larger groups at any time. When learning,
students could use digitally enhanced printed
materials and supportive digital content, accessible
through shared CLUSPI readers [2]. For gathering
proper feedback about group formation,
interaction in groups, and group disbanding we
have suggested employing RFID tags assigned to
students and compact handheld RFID readers
integrated with CLUSPI devices.
In this work, along with the continued
discussion of RFID-based authentication, we also
consider biometric methods for user identification
and authentication [3]. Since in our experiments
we work with elementary school children, we
believe that the solution eliminating the need of
digitally enhanced user IDs and using purely
biometric data might be more suitable [4].
2. Methods and technological means for
transparent user authentication
Transparent user authentication refers to
recognizing people on the fly, without disturbing
or interfering with their current activities. Such
identification happens, for example, in quite a
natural way when people interact between
themselves. In human societies, we can quickly
recognize each other by the way we look, by the
way we speak, and by the many other distinctive
features that make us unique.
For a computer system, however, recognition
and authentication of humans on the fly is far from
a trivial task. Consequently, instead of addressing
this problem, many of the commercial user
authentication systems would rather rely on
digitally enhanced user IDs. Those could be in the
form of badges with barcodes, magnetic stripes,
embedded RFID (Radio Frequency Identification)
tags, etc. RFID-based methods seem to be
particularly suitable for transparent user
identification, since they provide instant access to
ID data without contact or clear line of sight
requirements [5]. Our RFID-related studies and
experimental work are reported in the following
sections.
A potentially better solution for transparent
user identification would be to eliminate the need
of digitally enhanced user IDs and to use purely
biometric data [3,4]. Here we present a nonexhaustive list of currently available biometric
authentications methods that could be presumably
employed for transparent user identification:
 Face Recognition
 Voice Recognition
 Signature Recognition
 Iris Recognition
 Retina Recognition
 Fingerprint Recognition
 Hand Recognition
 Finger Geometry
 Palm Geometry
People are good at recognizing human faces
and voices, but unfortunately currently available
face and voice recognition methods and algorithms
lag far behind, and remain of limited practical use.
Other enlisted biometric methods such as iris,
retina, and signature recognition require voluntary
user actions and thus are not suitable for
transparent user authentication.
In human-computer interaction, the two most
commonly used input devices are the keyboard
and the mouse. During the interaction process,
humans are constantly in contact with these input
devices. Therefore, for a truly transparent user
authentication, we would only need to make the
computer sense and recognize human fingers or
hands in the course of the input process. We thus
mark the hand and finger related biometric
methods on the above list as good potential
candidates for more detailed studies and
experiments. Further discussions and details about
appropriate input devices with built-in biometric
sensors,
suitable
for
transparent
user
authentication will be given in the following
sections.
The proper selection of RFID tag types, their
sensing ranges and the way users wear them thus
becomes a very important issue. Limited range
hand-wearable RFID tags, for example, would be
most suitable for properly tracking the person
currently in charge of the input. Wider range RFID
badges, on the other hand, would suit for
identifying members of a group, gathered in front
of a computer monitor.
The method that we
chose to employ is based on wearable RFID rings
and on compact RFID readers, integrated with
traditional input devices such as keyboards, mice,
handheld scanners, and CLUSPI readers. For
illustration we show in Fig.1(a) several such rings
created by attaching button-type RFID tags to
magic tape stripes and conventional plastic rings.
(a)
(b)
Figure 1. RFID rings (a) and a RFID mouse (b).
The RFID rings can be used when operating
RFID-enabled keyboards as discussed in [7] or the
RFID mouse shown in Fig.1(b).
2.1. RFID-based user authentication
Currently available RFID technologies work
with passive tags of different shapes and sizes that
can be sensed from a few centimeters to a few
meters and with active, battery powered tags that
can operate at even larger distances [6]. In contrast
to optical technologies like barcoding, where a
clear line of sight is always required, the RFID
tags can be accessed even in presence of visual
obstacles. RFID technologies are also more stable
and prone to damages and errors, and do not
require contact or close proximity as magnetic
stripes.
In the context of e-learning, truly transparent
user identification and tracking of user activities
could only be achieved if users do not have to take
any conscious actions to authenticate themselves.
Figure 2. The combined RFID-CLUSPI reader.
For our experiments we have also prepared an
integrated RFID-CLUSPI reader that combines
the CLUSPI direct point-and-click functionality
[8] with the RFID user identification. For
illustration, a user wearing one of the rings and
holding the developed RFID-CLUSPI reader is
shown in Fig.2.
User identification is conducted on the fly in a
transparent and unobtrusive way, whenever users
interact with the RFID-enabled device. In this way
collaborative groups of learners, sharing existing
e-learning environments, could be created and
managed.
2.2. Fingerprint-based user authentication
RFID-based authentication methods discussed
in the previous section require all users to wear
RFID tags which may cause some inconveniences,
especially when children and elderly people are
involved [4]. In this context, alternative
approaches, such as fingerprint identification,
vascular pattern recognition and other biometrics
authentication methods that do not require tagging
are worth considering. Fingerprint authentication
is a mature technology and affordable
development systems and software kits are
available from different makers. We will, therefore,
continue with a more detailed discussion of some
current fingerprint authentication equipment and
supporting software development tools.
becomes an alternative, or a complete replacement
of the standard password-based user authentication
embedded in the operating system. While this is a
convenient approach that establishes fingerprintbased control over the user access to system
facilities, software and data, it does not allow more
granular, application level access management and
control. For access to such lower level
functionality appropriate SDK and tools, which
are provided only at the discretion of the
fingerprint reader producer, are necessary. Based
on this criterion we have acquired from RATOC
Systems, Inc. the fingerprint reader hardware,
software and development tool set shown in Fig.4.
Figure 4. The fingerprint reader hardware,
software and tools from RATOC Systems, Inc.
The currently available SDK supports only
Windows-based development and thus for now we
conduct all our experimental work on Microsoft
Windows machines.
(FAM) Fingerprint Authentication Module
Input of
Fingerprint
Images
Feature
Extraction
User
Database
Search
Registered
User
Database
Authenticated
User ID
(FRD)
Fingerprint
Reader
Device
Figure 3. Fingerprint authentication system.
A typical fingerprint authentication system
(Fig.3) consists of a fingerprint reader (1)
connected to a computer (2) and fingerprint
authentication software (3) that integrates with the
operating system of the employed computer. In
this way, fingerprint-based user authentication
E-learning Application
(UMM) User Management Module
User Profile Data Queries
User ID
Input
Profile
Data
Request
Profile
Data
Input
Figure 5. Structure of the developed fingerprint
identification software.
The structure of the developed fingerprint
identification software and its integration with
separately built applications is shown in Fig.5. The
Fingerprint Authentication Module (FAM) first
obtains fingerprint images from the fingerprint
reader through a USB connection. Feature
extraction algorithms are then applied to the
acquired images and, based on the result, a search
in the user database is conducted. If a positive user
authentication is obtained, the corresponding user
ID is sent to a separately built application directly
or via the User Management Module (UMM) as
depicted in Fig.5. Note that the FAM module
operates in a continuous mode and produces a
stream of authenticated user IDs as long as a
properly registered user finger stays within the
sensing range of the fingerprint reader device. This
functionality is a basis of the core transparent user
authentication that we propose and allows
separately built applications to get notifications of
user changes in real time.
Some fingerprint reader devices, however,
appear to lack continuous fingerprint input
capabilities. The device shown in Fig.6, for
example, requires a sliding finger gesture to be
performed for every fingerprint input. Obtaining a
stream of authenticated user IDs is then only
possible if the user constantly slides his finger
over the sensor, which obviously does not
constitute an acceptable interaction method.
Figure 6. A sliding fingerprints reader.
To implement truly transparent user
authentication one would actually need a
fingerprint reader device integrated with other
traditional input devices in such a way that users
do not even notice its presence. The fingerprint
mouse shown in Fig.7 is one such example and we
are considering using it for comparative studies
during our experimental work. When a user holds
the fingerprint mouse his thumb stays within the
sensing range of the embedded fingerprint reader
and thus allows continuous sensing and streaming
of fingerprint images. Once the user releases the
mouse, however, his thumb leaves the fingerprint
reader sensing range and the streaming of
fingerprint images is interrupted. In this way
multiple users could share a single mouse and still
the current user would be always positively
identified.
Figure 7. A fingerprint mouse.
There are also fingerprint keyboards that
employ an embedded fingerprint reader for user
authentication.
Unfortunately,
with
such
keyboards, fingerprint scanning and typing are two
independent processes that cannot be easily
integrated. It is not difficult to envisage, however,
that future keyboards might have contact
fingerprint scanners embedded in all their keys
and then, simultaneous typing and authentication
would become possible to implement.
Similarly to the fingerprint mouse in Fig.7 we
are considering an integrated fingerprint-CLUSPI
input device. It could be constructed by combining
a compact fingerprint reader with a standard
CLUSPI device in a way much similar to the one
employed for building the integrated RFIDCLUSPI device shown in Fig.2. The resulting
fingerprint-CLUSPI reader, although being a
single integrated device, could still function as two
independent devices with separate USB
connections. In this way no special software for
the integrated device would be need and the
standard drivers and previously developed
CLUSPI related and fingerprint authentication
based applications could be used with no
modifications.
3. Digitally-enhanced collaborative e-learning
environments
We are currently developing a new
collaborative e-learning environment model with
extended support for adaptive applications and
transparent user identification. Based on it, an
augmented interactive desktop that allows shared
use of digitally enhanced physical objects as
tangible interface components, is being
constructed [9]. The augmented interactive
desktop could be used in practical experiments
with digitally enhanced printed handouts for
elementary math tutoring and language learning
[10]. A schematic diagram of the currently
developed software system is shown in Fig.8.
User
Database
(FRD)
Fingerprint
Reader
Device
Additional
Authentication
Device(s)
(RAM) RFID
Authentication
Module
(FAM) Fingerprint
Authentication
Module
Additional
Authentication
Module(s)
FAM
Section
Other
Section(s)
Authenticated User ID
(UMM) User
Management Module
User ID Interface
RAM
Section
User Profile Data
(RRD)
RFID
Reader
Device
User Profile
Data Queries
User Profile Access Interface
E-learning Application
E-learning Application
Figure 8. A schematic diagram of the developed
software system.
The system has a modular structure and is
specifically designed to support different user
authentication methods. At present we have a fully
functional prototype of the RFID authentication
module while the fingerprint authentication
module is still under implementation. Additional
user identification modules will also be developed
and added to the system in the future.
Since different authentication methods could
employ different input devices, a default
authentication method is supplied to the system
during the initialization. Based on that, an
appropriate user authentication module is selected
and access to the corresponding user database
sections is ensured.
When a user is authenticated by any of the
available authentication modules its unique user
ID is sent to the UMM module as explained in the
previous chapters. Here we will continue with an
outline of the UMM module functionality.
The UMM module is mainly responsible for
providing independently developed applications
with user related data, extracted from the user
database according to their needs. One application
may need to know, for example, the age of the
users while another may need their genders.
Instead of allowing every application to query the
user database independently, one could gather and
process all the queries within the UMM module
and thus ensure more efficient and secure database
access. Following this model, applications could
register the user profile parameters of interest with
the UMM User Profile Access Interface.
Afterwards, whenever a user is successfully
authenticated, the UMM will be automatically
providing the required user data to the relevant
applications.
4. Experimental framework
elementary math tutoring
for
adaptive
In this section we discuss some practical
aspects
of
the
collaborative
e-learning
environment model presented earlier. Our
objective here is to build an experimental
framework, based on the model and employing
educational content directly applicable in school
tutoring.
Elementary math tutoring has always been
recognized as a demanding process, which is
expected to set up the foundations of abstract
thinking and to promote interest in exact sciences
since childhood. It is important, thus, to enhance
and diversify traditional math lessons so that
students become more engaged through e-learning
and interactive activities. In our experimental
implementation of the collaborative e-learning
environment model, we provide digitally enhanced
educational materials for elementary math tutoring
that are suitable for class work, groupwork and
self-studies [10].
We implement the augmented interactive
desktop as a standard tabletop with digitally
enhanced picture cards and an RFID-CLUSPI
reader. A single student could use the cards to
answer automatically generated elementary math
problems by pointing and clicking with the RFIDCLUSPI reader on the cards. In a group of two or
more students, participants could take turns to
formulate and to solve problems. In a class, the
teacher could formulate problems, shown on a
large screen and students could register their
answers simultaneously. Digitally enhanced
picture cards could also be used for building
educational games, such as the elementary math
domino shown in Fig.9.
Figure 9. Elementary math domino.
In all cases, the system obtains feedback about
the current user by sensing his RFID tag. This
functionality is highly desirable even for
individual use, since such transparent user
authentication gives instant access to the user
profile and his learning history. Changeable data
related to the current user could be stored locally
by the application, in the user database or on the
RFID tag. In the first two cases, when user
changes computers, e.g. a school computer and a
home computer, the stored learning history has to
be explicitly communicated over the network or
transferred by a memory stick, etc. When the
RFID tag is used, however, the user history always
stays with him, embedded in his RFID, so much
simpler infrastructure becomes possible.
5. Conclusions and future work
In this work we have reported a new approach
for building digitally enhanced collaborative elearning environments incorporating transparent
user recognition and authentication through RFID
tags and biometric data. The approach has been
applied to the implemented augmented interactive
desktop and will be employed in adaptive
elementary math tutoring experiments in
collaboration with teachers at local elementary
schools.
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