Intelligent Classroom

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The Intelligent Classroom: Towards an Educational Ambient Intelligence Testbed
Rabie A. Ramadan
Computer Engineering Department, Cairo University, Cairo, Egypt, [email protected]

Hani Hagras, Moustafa Nawito, Amr El Faham, and Bahaa Eldesouky,
Ambient Intelligence Center, German University in Cairo, New Cairo City, Egypt hani,@essex.ac.uk embedded in ambient intelligence environments have been presented. In the field of education, ambient intelligence also plays a key role. For instance , there are some efforts that have been done in this regard including North Carolina State University’s Web Lecture System (MANIC) [10], the Berkeley Multimedia Research Center’s Lecture Browser [7], , AutoAuditorium [1], STREAMS [9], and AutoTutor [12]. The recent advances in RFID technology made it possible to somehow to have the advantages of using passive tags with high frequency ranges. These RFIDs have been used in many applications; for example, it has been used for person identification as in universities and/or companies. Nowadays, new passports save information like, a digital picture of the owner, a digital version of the passport and biometric information about the owner in the passport's RFID tag permanently. There are many other applications that involve RFID usage in hospitals, animal identification, transportation, stores payments, and banks [4]. Figure 1 shows the market in terms of RFID tags sold for different purposes. As can be seen, the RFID technology is used most for retail apparel while is almost neglected for people identification; only 1.3 million tags are sold for this purpose. However, we believe that our iClass is one of the fields that prove the importance of RFID in educational smart environments. In this paper, we introduce a unique testbed for educational ambient intelligence classroom (iClass) where different AmI techniques and algorithms have been exploited. The paper describes the iClass architecture (as part of Ambient Intelligence center at German University of Cairo (GUC)) in terms of hardware and networking. Section III, portrays the importance of RFID technology in classroom environment. Section IV presents how the user can interact via speech with the iClass. Section V present how the iClass can learn the user behavior and adapt to it over short and long time intervals.

Abstract— The widespread of embedded computer networks as part of everyday peoples’ lives is leading the current research towards smart environments and Ambient Intelligence (AmI). AmI is a new information paradigm where people are empowered through a digital environment that is “aware” of their presence and context and is sensitive, adaptive and responsive to their needs. In this paper, we describe the intelligent Classroom (iClass) which aims to realize the AmI vision in Education in universities and schools. We will describe the architecture employed to build the iClass and we will present three different directions including the utilization of RFID technology, interacting with the user via speech and developing intelligent agents to learn the user behavior and adapt to its change over short and long time intervals. Keywords- intelligent classroom; sensor networks, fuzzy logic, RFID

I.

INTRODUCTION

Mark Weiser described the smart environments as physical worlds that invisibly interact with smart sensors, actuators, displays, and computational elements that are seamlessly implanted into our daily live activities. However, smart environments have to be associated with different Artificial Intelligence (AI) techniques and algorithms including artificial neural networks, evolutionary computation, swarm intelligence, artificial immune systems, and fuzzy systems. Together with logic, deductive reasoning, expert systems, case-based reasoning and symbolic machine learning systems, these intelligent algorithms help in forming smart environments. Combining the AI techniques and algorithms with smart environments leads to new research field named “Ambient Intelligence (AmI)“. AmI is defined as an electronic environment that is sensitive and responsive to the presence of people in specific environment. AmI techniques and algorithms have been utilized in many of smart environments research. For instance, at university of Essex [11] the authors tried to achieve the vision of ambient intelligence by embedding intelligent agents in the user environments so that they can control them according to the needs and preferences of the user. A novel fuzzy learning and adaptation technique for agents that are

browser using HTML forms which passes its data to a notepad file created by a parser java program that the agent read its input from. Most of the sensors and effectors in the iClass are connected via a Lonworks network. The Echelon‘s i.LON SmartServer shown in Figure 5 is the key to business‘s energy conservation and operations strategies. It not only lets us access, control, and monitor virtually any electronic device the iClass, but it also gives the power to use information intelligently to save energy, improve operations, and lower maintenance costs.

Figure 1: RFID market in different areas

II.

ICLASS ARCHITECTURE

The iClass as shown in Figure 2 is a test bed for educational ambient intelligence system. The iClass looks like any other classroom containing normal furniture as in a usual room, including the desks, chairs, white board and a smart board. However, the iClass consists of a large number of embedded sensors, actuators, processors and a heterogeneous network. The iClass is a multiuser space that can be used through different teaching activities. As shown in Figure 2, there is a standard multimedia PC that combines a projector with a flat-screen monitor and another digital monitor, which is placed outside the class to inform students with the starting and ending time, name of the lecture topic and any other announcements related to the given course as shown in Figure 3. Figure 4 shows the iClass network infrastructure. The iClass is equipped with a weather station. In addition the iClass has the following sensors and actuators: time of the day and date, internal light level sensor, external light level sensor, internal temperature sensor, external temperature sensor, humidity sensor and a presence sensor. The effectors can control the following in the class: six dimmable spot lights, two window blinds and heater/cooler air conditioning. These sensors and actuators are obscured in the class with the intention that the user should be completely unaware of the intelligent infrastructure of the class, which is required to reach the aim of educational ambient intelligence. Although the iClass looks like any other class, the ceiling and walls hide numerous networked embedded devices residing on two different networks: Lonworks and IP network. These networks provide the diverse infrastructure present in ubiquitous-computing environments and let us develop network independent solutions. Because we need to manage access to the devices, gateways between the different networks are critical components in such systems, combining appropriate granularity with security [6]. Lonworks, Echelon‘s proprietary network, includes a protocol for automating buildings. Many commercially available sensors and actuators exist for this system. The physical network installed in the iClass is the Lonworks iLON Smartserver network which provides the gateway to the IP network. This server lets us read and alter the states and values of sensors and actuators via a standard Web

Figure 2: iClass internal view.

Figure 3: iClass external view.

Figure 4: iClass network infrastructure

Figure 5: Echelon’s i.LON® SmartServer [8]

Figure 6 shows photos of the various sensors and weather station located with the iClass. The weather station is installed outside the iClass to measure the outdoor humidity, cloud cover, wind direction, wind speed, rain fall, solar radiation and the outdoor temperatures. Any networked computer that can run as standard Java process can access the iClass, thus, this multimedia PC can also act as an

interface controlling the devices inside the class room. Equally, the interface can be accessed from wireless devices such as the mobile phone using a 3G interface, which is a simple extension of the web interface, which can monitor and control the iClass directly. Currently our fuzzy agent learning mechanism and interface operates from the standard multimedia PC in the iClass. III. RFID IN ICLASS In this section, the role of RFID technology in iCLass is explained. There are two RFID readers as shown in Figure 4; one for the lecturers and the other one for students. Each lecturer has an RFID tag that includes the lecturer identifier (ID). Once the lecturer enters the iClass, the lecturer RFID reads his/her ID and sends it to the multimedia computer. A smart agent is designed specially to deal with this information. The smart agent is designed to lookup the classroom schedule out of the school schedule and get 1) the classroom assigned lecturer name and ID at this time, 2) the students names and IDs that are currently assigned to the classroom at this time, and 3) a copy from the lecture materials that were uploaded by the lecturer before the lecture time. The agent is also responsible for turning on the data show and the smart board and shows the materials on the smart board. On the other hand, once the lecturer is recognized and students start to enter the class room, the students RFID reader begins to read their RFID tags and sends this data to the multimedia computer as well. The Students’ process is similar to lecturer process; however, a timer and a number of times to read are set to the RFID reader to read the students tags. To evaluate the overall RFID system, a software agent has been implemented using dot net on the iClass multimedia computer to utilize the automatic attendance of students during last semester on one of the subjects. The performance of such system is tested against manual attendance and found that the automatic attendance system accuracy is on average 97% which are acceptable results. The other 3% error percentage was due to the time threshold that we set and/or the problem with RFID signals. The time threshold that we set restricts the student attendance to half of the lecture time while manual attendance (lecturer takes the attendance by himself/herself) does not have this condition. The problem with the RFID signals could be due to students putting their cards on a wallet and put them on their back bucket, have other cards with them or unethical issues such as a student having other classmates’ cards. IV. SPEECH INTERACTION WITH THE ICLASS

the Internet which reflect the dominance of spoken communication in many of the human psychological aspects.

Figure 6: The iClass sensors, weather station and multimedia video projector.

Speech communication is an essential part of human psychology. In fact, through the speech communication human symbolic behavior can be studied. It is also one of the oldest academic discipline as well as one of the most modern academic interests. However, speech communication is not only limited to human interpersonal communication, but also extended through technological mediation such as telephony, movies, radio, television, and

The challenge is in designing spoken communication language between the human and the computer where the computer can listen, speak, understand and more importantly to learn. Therefore, it is expected with modern technology, the current interest will be in developing voice controllable systems. it is also expected that the human-machine spoken language will change the way we live and work [14]. One of the challenges in iClass is to allow speech interaction with its users. Since iClass software was built with modularity in mind, we were able to import one of the speech recognition library named “Sphinx-4 [13].” In iClass speech interaction, we utilized the features introduced in Sphinx-4 library for the benefit of iClass environment control. Along with Sphinx-4 speech recognition library, we had to define our grammar for iClass control. This grammar includes, Open light, Close light, Amplify light, Decrease light, Open curtain, Close curtain, Amplify curtain, Decrease curtain, Open air condition, Close air condition, Amplify air condition, and Decrease air condition commands. In addition, we designed a fuzzy agent named Speech Recognizer Based Intelligent Fuzzy Agent (SRBFA). It is based on unsupervised data-driven one-pass approach for extracting fuzzy rules and membership functions from data to teach a fuzzy controller that will model the user’s behaviors. The data is collected by monitoring the user in the environment over a period of time. The learned Fuzzy Logic Controller (FLC) provides an inference mechanism that will produce output control responses based on the current state of the inputs. Our adaptive FLC will therefore control the environment on behalf of the user and will also allow the rules to be adapted and extended online, facilitating life-long learning as the user’s behavior drifts and environmental conditions change over time. SRBFA is comprised of five phases in addition to the environment readings, as shown in Figure 7, :1) monitoring the user’s interactions and capturing input/output data associated with their actions (the user input is done through speech and interface; 2) extraction of the fuzzy membership functions from the data; 3) extraction of the fuzzy rules

from the recorded data; 4) the agent controller; 5) life-long learning and adaptation mechanism.
Capture data on user interaction Extract membership function

Extract Fuzzy rules Recognize speech from user Agent control and online creation/adaptation to fuzzy rules

Figure 8: The number of rules learned during the experiments.

Environment

V.
Figure 7: SRBFA phases

AN INTELLIGENT AGENT TO LEARN AND ADAPT TO THE USERS’ BEHAVIOURS

It is necessary to be able to categorize the accumulated user input/output data into a set of fuzzy membership functions which quantify the raw crisp values of the sensors and actuators into linguistic labels, such as normal, cold, or hot. SRBFA is based on learning the particularized behaviors of the user and, therefore, requires these membership functions to be defined from the user’s input/output data recorded by the agent. A clustering approach [2] based on fuzzy-C-means (FCM) clustering was used for extracting fuzzy membership functions from the user data. Our dataset of user instances contains many attributes. We start by generating p initial clusters using the FCM approach. Each cluster has a center , which is an rdimensional vector having r centroid values . The final cluster centers are then converted to the extracted fuzzy sets (linguistic labels).We used that algorithm because it is able to learn the individual behavior of the user. Different memberships were generated for different users due to the different behaviors of the users observed when the iClass interface was used in the first experimental phase. To study the performance of our speech interaction system, we conducted different experiments. In one of these experiments the user had to spend three consecutive days inside the iClass. Once the user entered the iClass, he recorded a voice sample which allowed the system to recognize the speaker successfully and created the user profile to associate the fuzzy rules with as it was the first time for the user to use the classroom. As shown in Figure 8, during the first day, the user had to define the meaning of each voice commands to the system on different environmental conditions. The system rate of learning new rules was the highest on that day. As any surrounding condition is changed while adapting the class room, the system had to generate the new rules that are relative to this adaptation. On the second day the user was not satisfied by all the adaptation applied by the classroom when voice command is given. The user had to override some rules to adapt the system again according to the new situation. On the third day, the system has stabilized as the user was satisfied by the adaptations that occur when he gave voice commands and no more overriding occurred.

Fuzzy logic is proved to provide a good framework for modeling various types of uncertainties in information. Fuzzy Logic Controllers (FLCs), the most popular application of fuzzy logic, provide an adequate methodology for designing robust controllers that are able to deliver satisfactory performance when contending with the uncertainty, noise and imprecision attributed to real world environments. However, the linguistic and numerical uncertainties associated with dynamic unstructured environments cause problems in determining the exact and precise antecedents’ and consequents’ membership functions during the FLC design. Type-2 fuzzy logic is an extension of ordinary type1 fuzzy logic where the membership function is fuzzy rather than crisp. As shown in Figure 9, in type-2 FLCs, the crisp inputs from the input sensors are first fuzzified into input type-2 fuzzy sets. The input type-2 fuzzy sets then activate the inference engine and the rule-base to produce output type-2 fuzzy sets. The type-2 FLC rule-base is the same as that of a type-1 FLC (i.e. a set of IF … Then … rules). The only difference is that for type-2 rule bases, the antecedents and/or the consequents will are represented by type-2 fuzzy sets. The inference engine combines the fired rules and gives a mapping from input type-2 fuzzy sets to output type2 fuzzy sets. The type-2 fuzzy outputs of the inference engine are then processed by the type-reducer, which combines the output sets and performs a centroid calculation that leads to type-1 fuzzy sets called the type-reduced sets. The type-reduced sets are then defuzzified to produce crisp output values. Our agent operations can be divided into the following phases (as shown in Figure 10): A. Building individual type-1 fuzzy profiles for input/output variables. B. Building the type-2 model for input/output variables C. Monitoring users’ behavior D. Generating the type-2 FLC E. System control and adaptation F. Rule-base optimization In the following subsections, these phases are explained in some details.

the fuzzy rules from the user data in the current phase we have a type-2 FLC that models the users’ behavior in the environment, which makes the system FLC ready to operate the iClass on behalf of its occupants.

Figure 9: Structure of a type-2 FLC

A.

Building individual type-1 fuzzy profiles for input/output variables
Figure 10: Phases of operation of the proposed system

The agent starts by modeling individual type-1 fuzzy profiles that encapsulate the preferences of individual users. These sets are acquired by two different methods. In the first method, the agent is adjusted to automatically monitor the iClass users in the classroom for a certain period of time and extracting their fuzzy profiles using some techniques such as Fuzzy C-Means clustering (FCM) technique [5]. The second method was intentionally designed to be more into manual process. The iClass users are asked to fill in a carefully crafted survey in which the users fill in only few values for each fuzzy variable. B.

E.

Agent control and online adaptation

Building the type-2 model for input/output variables

In this phase the system aggregates the individual type1 profiles to produce the type-2 fuzzy model for the input/output variables. The aggregated type-2 model characterizes the collective behavior of the class occupants making use of type-2 fuzzy logic capability of incorporating higher levels of uncertainty. It effectively models the uncertainties present in the environment especially the interuser uncertainties about the meanings of input/output variables. C. Monitoring users’ behavior After building type-2 models, the system then starts to monitor users’ actions in the environment to incrementally build the system fuzzy rule base. Based on the IAOFIS approach [3], whenever a user changes actuator settings, the system records a “snapshot” of the current inputs (sensor states) and the outputs (actuator states with the new altered values of whichever actuators were adjusted by that user). The set of accumulated multi-input multi-output data pairs are then used to construct the rule-base of the system type-2 FLC. D.

Once the system FLC rule-base is ready, the system can take control of the environment. The system FLC regularly reads sensory values and fuzzifies them into type-2 fuzzy sets. It then uses the rule-base to do inference on the input sets and produce the type-2 output fuzzy sets representing the decision taken on behalf of the users which reflects their learnt behavior. These type-2 sets are then type-reduced to produce type-1 fuzzy sets which are then defuzzified into crisp values used to drive the different actuators in the classroom. The system not only controls the environment reproducing the users’ behaviors but also has adaptation capability. There are two types of adaptation that the system can perform: 1. Short term online adaptation: whenever a user intervenes by actuating one or more of the classroom actuators to override a control action by the system, the system records these interventions and updates the rule-base accordingly online. 2. Long term adaptation, as the changes in users’ behavior or in the operation conditions accumulate the amount of uncertainty that the system has to model becomes big enough to degrade the system performance. The system transitions to long term adaptation by jumping back to phase 3 where users are monitored again to rebuild the FLC rule-base to more accurately reflect their preferences. F.

Rulebase optimization

Generating the type-2 FLC

Now, the set of interval type-2 membership functions generated from phase 2 are combined with the accumulated user input/output data to extract fuzzy rules defining users’ collective behavior. After generating the interval type-2 membership functions in the previous stage and generating

The explosion of the rule-base size is a major problem in rule-based systems that arises from redundancy in the rules. In this phase of operation, the system optimizes the rule-base size by tackling both attribute redundancy and rule redundancy. In most of the optimization experiments, the rule-base to-be-optimized had nine input variables: Time-ofday, inside light, outside light, inside temperature, outside temperature, humidity, wind speed, wind direction and occupancy.

Figure 11 plots the number of eliminated attributes due to insignificance versus the number of rules in the rule base. At rule-base size 1350, the percentage of attributes eliminated was 44.4% which is nearly half of the antecedent attributes of the rule base. The optimization phase thus, not only helps us to reduce the size of the rule-base and enhances the overall performance; it also helps extract the most significant attributes of the users' behavior. The elimination of irrelevant attributes leads to substantial reduction in the size of the rule base. After discarding the irrelevant attributes (i.e. decreasing FLC input dimensionality) duplicate rules in the rule-base are eliminated and the size of the rule-base shrinks significantly.

VI.

CONCLUSION

In this paper, we introduced the architecture of our intelligent classroom (iClass) in terms of hardware and software. In addition, we explained three main components of the iClass which are RFID , speech interaction, and users behavior components. Fuzzy logic is utilized in these main components where a novel Type-2 fuzzy approach is proposed and implemented to capture the iClass users’ behaviors. The Type-2 fuzzy approach is also used to control the iClass different actuators according to the iClass occupants. Through a set of experiments, the results proved the efficiency of our design as well as the used techniques and algorithms. REFERENCES
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Figure 11: The number of rules vs the number of eliminated attributes

[3]

To appreciate the reduction in the rule-base size due to irrelevant attribute elimination the following example it suffices to say that eliminating 4 attributes from the input set of our system resulted in a 99.65% reduction in size. G.

[4] [5]

Intelligent Agent Evaluation

[6]

To evaluate our system’s performance, we ran the system controlling the environment for 48 hours with 4 users and recorded the number of rule-base updates that measures users’ satisfaction with the system. The system operated 6 input variables; Time-of-day, inside light, outside light, inside temperature and outside temperature and occupancy. It controlled 4 output type-2 fuzzy variables: front window blinds, rear window, front dimmable lights, rear dimmable lights. Figure 12 shows the cumulative number of rule-base updates due to user dissatisfaction with the system behavior or due to encountering new points in the control surface that haven't been covered during the monitoring phase. Rule-base updates have been recorded every triple of hours. Figure 12 clearly suggests growing user satisfaction with the system which acceptably gets to a stable level where few rule updates are required every now and then due to uncovered points on the control surface or an occasional change in the users' behavior.

[7] [8] [9]

[10]

[11]

[12]

[13]

[14]

Figure 12: The cumulative number of rule_base updates (adds or modifications) vs the operation time.

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