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![Surveys in literature (2/2)
Survey Topics Type
Technology
Learning analytics Ferguson el at. [8]
Data mining in education Romero & Ventura [9]
Learning management system Kelecs et al. [22]
Augmented reality in classrooms Chen et al. [2]
Automatic attendance with RFID
and Face recognition
Patel et al. [23]
Technology impact
and acceptance
Interactive white-boards
Glover et al. [24],
Higgins et al. [25],
Martin et al. [26],
Raman et al. [14]
Classroom Response Systems
(CRSs)
Files et al. [27]
Audience response systems(ARSs) Kay et al. [28]
Distance learning Zawackiet al. [29]
Twitter and microblogging for
communication
Ha et al. [30]
eLearning Gallagher et al. [13]
Smartphones in classroom Langmia and Glass
[31]
Immersive environment,
mixed-reality environment
Gardner et al. [32]
Pedagogy
Flipped classroom Findlay et al. [33] and
Zhao et al [34]
Multimedia technology and web
courses
Parker et al. [35]](https://image.slidesharecdn.com/smart-classroom-200219032006/75/Smart-classroom-6-2048.jpg)
![Main content preparation methods
Method Software used Hardware Used Challenges
Presentations
[54][55]
Microsoft Office,
Openoffice,
Keynote, Prezi
Desktop, Cloud in case
of Prezi [51]
Difficult to automate,
time consuming
Animations
[56][57]
Microsoft Office,
drawings, Animwork,
Mixeek, Wideo
Desktop
Requires professional help,
time consuming
Video
[33][58][59][60]
[61][62]
Recording software
PTZ[63][64],
mobile camera [65],
camcoders[66], Kinect
[67],
microphones [60]
Synchronization, indexing,
scalability, camera control
Augmented-
Reality, Virtual-
Reality [53] [52]
Computer vision and
computer graphics
methods
Workstations, special
calibrated cameras
Expensive, time
consuming, need
professional help](https://image.slidesharecdn.com/smart-classroom-200219032006/75/Smart-classroom-13-2048.jpg)
![Main content presentation tools
Hardware Purpose
Interactive white boards [25] [24] Distance learning
TV [70] [5] To explain complicated concepts using videos
Projectors [71] [4] To view slides and power point presentations
Multiple projectors/ large displays
[16]
Immersive environment
Kinnect sensors [72] [1] Gesture based control
Robots[76] [77] [75] Predefined discussions, interactivelearning
Smartphones [74] Controlling slides andprojector](https://image.slidesharecdn.com/smart-classroom-200219032006/75/Smart-classroom-18-2048.jpg)
![Engagement modeling parameters and
automatic measurement
Parameters Sensor Output
Fighting [21] PIR sensor and
camera
Motion existence and level
Noise [21] Microphone Noise existence
Sound level [21] Sound sensors Sound level
Instructor’s motion [11] Accelerometer Instructor’s motion
Closed eyes [106] PTZ cameras Eye detection
Head nod [107][12] Camera Face detection
Attentiveness [108][109] Camera Eye gaze](https://image.slidesharecdn.com/smart-classroom-200219032006/75/Smart-classroom-31-2048.jpg)
![Main engagement enhancement methods
Engagement
Aspect
Works Hardware Used Tasks
Student-teacher
interaction
ActiveClass
[110]
Mobile devices Anonymous
questions/feedback
Bargaoui et
al. [82]
Tablets, laptops, and
projectors
Communication getaway
for interaction
Liu et al.
[16]
Projectors, tablets Questions usingtablet
Tai et al. [104] Interactive whiteboard, a
projector, a laptop, and remote
control
Response through remote control
Student-student
interaction
Abut and
Ozturk [70]
Desktop, projector,
mobile electronicdevices
Team building,
collaboration
Yau et al. [7] PDAs (mobile devices) Project group
collaboration
Bargaoui et
al. [82]
Tablets, laptops, and
projectors
Collaboration withother
students
Ha and Kim
[30]
Mobile phones, desktops Interactions with
microblogging
Ishida [111] Anydevice withdisplay Collaboration among
multi-language students
Student-content
interaction
Classtalk [5]
Desktop, palmtop,
projector, communication network
Question/response presentation
Kaufmann
et al. [112]
AR equipment Collaboration, 3D
concept presentation
Liu et al.
[16]
Projectors, tablets Immersive environment](https://image.slidesharecdn.com/smart-classroom-200219032006/75/Smart-classroom-35-2048.jpg)
![Plagiarism check
• Very hard to automatically check handwritten
assignments
• A number of tools for plagiarism check in
online submissions
• Text plagiarism [134]: Turnitin, Urkund,
Copycatch, Wcopyfind, GPSP, Ithenticate
• Code plagiarism [134]: MOSS, Jplag,
Plagiarism-Finder, Plaggie, SIM,](https://image.slidesharecdn.com/smart-classroom-200219032006/75/Smart-classroom-40-2048.jpg)
![Physical environment quality measures
Important Environmental
aspects
Desired Control mechanism
Temperature 24◦ to 26◦ Celsius [161] Any modern air conditioning sys-
tem is able to control air temper- ature
Humidity 40-60% [162] GSM-based system [163], Humid-
ity dehumidifier[164],Ventila- tion [165]etc.
Radiation none(except sunlight) Sealing the radiation sources or
the classrooms
Volatile Organic
Compounds (VOC)
below 200g/m3 [166] Mainly ventilation systems [167] and source
management
Nitrogen Dioxide(NO2)
-from burning fuelitems
annual average below
below 0.02ppm, hourly average
below 0.11 ppm [168]
Mainly ventilation systems [167] and source
management
Carbon Dioxide (CO2) -
mainly emitted by humans below 800ppm[169] Mainly ventilation systems [167] and source
management
Airborne particles -
coarse dust particles (PM10) and
fineparticles (PM2.5)
PM10 below 150 ug/m3
daily average, PM2.5 below 35
ug/m3daily average [170]
Mainly ventilation systems [167] and source
management
Carbon Monoxide(CO) below 25ppm per hour
[171]
Mainly ventilation systems [167] and source
management
Sound level 25dB above noise level
[172]
Automatic noisemeasurement and audio
volumecontrol
Audio noiselevel below 49.6 dB [173] Sound insulation
Lighting 15003000Lux [173] Automatic light control [174]](https://image.slidesharecdn.com/smart-classroom-200219032006/75/Smart-classroom-49-2048.jpg)
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74 Ahmed Al-Hunaiyyan, Salah Al-Sharhan, and Rana Alhajri. A new mobile learning model in the context of smart classroom environment: A holistic approach.
International Journal of Interactive Mobile Technologies, 11(3):130–134, 2017.
75 Carlos Rodríguez, José L. Guzman, Manuel Berenguel, and Sebastián Dormido. Teaching real-time programming using mobile robots. IFAC-PapersOnLine, 49(6):10 –
15, 2016. 11th IFAC Symposium on Advances in Control Education ACE 2016.
76 Omar Mubin, Catherine J Stevens, Suleman Shahid, Abdullah Al Mahmud, and Jian-Jie Dong. A review of the applicability of robots in education. Journal of
Technology in Education and Learning, 1:209–0015, 2013.
77 Flor Ángela Bravo Sánchez, Alejandra María González Correal, and Enrique González Guerrero. In- teractive drama with robots for teaching non-technical subjects. J.
Hum.-Robot Interact., 6(2):48–69, September 2017.
78 Sang Hyun Kim, Clif Mims, and Kerry P Holmes. An introduction to current trends and benefits of mobile wireless technology use in higher education. AACE journal,
14(1):77–100, 2006.
79 Abdulhameed Alelaiwi, Abdullah Alghamdi, Mohammad Shorfuzzaman, Majdi Rawashdeh, M Shamim Hossain, and Ghulam Muhammad. Enhanced engineering
education using smart class environment. Computers in Human behavior, 51:852–856, 2015.
80 Ciaran O’Driscoll, Mohan Mithileash, Fredrick Mtenzi, and Bing Wu. Deploying a context aware smart classroom. In International Technology, Education and
Development Conference. Dublin Institute of Technology, 2008.
81 Koutraki Maria, Efthymiou Vasilis, and Antoniou Grigoris. S-creta: smart classroom real-time assis- tance. In Ambient Intelligence-Software and Applications, pages
67–74. Springer, 2012.
82 Hichem Bargaoui and Rawia Bdiwi. Smart classroom: Design of a gateway for ubiquitous classroom. In International Conference on Web and Open Access to Learning,
pages 1–4. IEEE, 2014.
83 Dexter H Hu. Smart classroom 2.0: Context-aware educational system. In Proceedings of the 17th International Conference on Computer Education, pages 10–41. Asia-
Pacific Society for Computers in Education, 2009.
84 Haojin Yang, Maria Siebert, Patrick Luhne, Harald Sack, and Christoph Meinel. Automatic lecture video indexing using video ocr technology. In IEEE International
Symposium on Multimedia, pages 111–116. IEEE, 2011.
85 Marco Furini, Silvia Mirri, and Manuela Montangero. Topic-based playlist to improve video lecture accessibility. In 15th IEEE Annual Consumer Communications &
Networking Conference, pages 1–5. IEEE, 2018.
86 Marco Furini. On introducing timed tag-clouds in video lectures indexing. Multimedia Tools and Applications, 77(1):967–984, 2018.
87 Manish Kanadje, Zachary Miller, Anurag Agarwal, Roger Gaborski, Richard Zanibbi, and Stephanie Ludi. Assisted keyword indexing for lecture videos using
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88 Matthew B Hoy. Deep learning and online video: Advances in transcription, automated indexing, and manipulation. Medical reference services quarterly, 37(3):300–305,
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Uploaded byNeeraj Goel
Smart classroom
The document reviews smart classroom technologies, highlighting their objectives such as smart content, interaction, assessment, and physical environment. It discusses various tools and methods for content preparation, engagement enhancement, assessment techniques, and managing physical classroom environments. Additionally, the document addresses the interconnectedness of these aspects and suggests the need for a unified framework for effective implementation.
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Smart classroom
- 1. How Smart areSmart Classrooms? A Review of Smart Classroom Technologies Dr. Mukesh Saini, Dr. Neeraj Goel CSE Indian Institute of Technology Ropar Link: https://dl.acm.org/doi/10.1145/3365757
- 2. What is asmart classroom? • A technology-assisted closed environment that enhances teaching and learning experience • Four major objectives – Smart content – Smart interaction & engagement – Smart assessment – Smart physical environment
- 3. Example of atypical smart classroom Projector Interactive whiteboard Smartinteractionandengagement Withcontent,instructorandstudents Smart content and presentation Smartassessment Automatedevaluationandinstantfeedback Smart physical environment Sensors and actuators to control environment
- 4. Smart classroom taxonomy SmartClassroom Smart content and presentation Smart interaction and engagement Smart assessment Smart physical environment Content preparation Engagement model Automated evaluation Air quality control Content presentation Engagement measurement Automated attendance Temperature and humidity Content distribution Automated feedback Sensors and actuators Engagement enhancement
- 5. Surveys in literature(1/2) • A number of existing surveys on smart classrooms • Technology surveys – Augmented reality, data mining, automatic attendance, LMS, CMS • Technology acceptance studies – Interactive whiteboards, immersive environments, eLearning, m-learning, distance learning • Pedagogy surveys – Flipped classrooms, web-based courses
- 6. Surveys in literature(2/2) Survey Topics Type Technology Learning analytics Ferguson el at. [8] Data mining in education Romero & Ventura [9] Learning management system Kelecs et al. [22] Augmented reality in classrooms Chen et al. [2] Automatic attendance with RFID and Face recognition Patel et al. [23] Technology impact and acceptance Interactive white-boards Glover et al. [24], Higgins et al. [25], Martin et al. [26], Raman et al. [14] Classroom Response Systems (CRSs) Files et al. [27] Audience response systems(ARSs) Kay et al. [28] Distance learning Zawackiet al. [29] Twitter and microblogging for communication Ha et al. [30] eLearning Gallagher et al. [13] Smartphones in classroom Langmia and Glass [31] Immersive environment, mixed-reality environment Gardner et al. [32] Pedagogy Flipped classroom Findlay et al. [33] and Zhao et al [34] Multimedia technology and web courses Parker et al. [35]
- 7.
- 8. Smart content types •An instructor mainly uses two types of content • Type 1: textbooks – Available as PDF or Hard copy • Type 2: Lecture support material – Slides, Graphics, Animations, Presentations – Videos and Audio – Whiteboard/blackboard drawings – Immersive environments
- 9. Smart content tools •For creating smart content – E.g. animations • Presenting smart content –E.g. immersive environments • Distributing smart content –Distance learning, eLearning
- 10. Smart content preparation(1/3) • Material used during the lecture delivery • Different form of media other than traditional chalk and blackboard • Presentation slides, animations, video, audio, etc.
- 11. Smart content preparation(2/3) • Presentations – MS Office, Openoffice, Keynote – Desktop environments – Google slides, Prezi – cloud based • Animations : software like, Animwork, Mixeek, Wideo • Augmented Reality (AR)/Virtual reality(VR) – Using applications given by device manufacture like Sony, HTC
- 12. Smart content preparation(3/3) • Creating videos – Lecture recording frameworks – Automatic post-processing – Useing multiple cameras and microphone to detect instructor location and create one video – Using PTZ camera to track instructor – Using mobile phone to record lectures
- 13. Main content preparationmethods Method Software used Hardware Used Challenges Presentations [54][55] Microsoft Office, Openoffice, Keynote, Prezi Desktop, Cloud in case of Prezi [51] Difficult to automate, time consuming Animations [56][57] Microsoft Office, drawings, Animwork, Mixeek, Wideo Desktop Requires professional help, time consuming Video [33][58][59][60] [61][62] Recording software PTZ[63][64], mobile camera [65], camcoders[66], Kinect [67], microphones [60] Synchronization, indexing, scalability, camera control Augmented- Reality, Virtual- Reality [53] [52] Computer vision and computer graphics methods Workstations, special calibrated cameras Expensive, time consuming, need professional help
- 14.
- 15. Traditional presentation tools •Instructor need visual presentation to support aural description – E.g., diagrams, architecture, graph • Initial use of technology in 90’s: TVs and projectors • Using multiple projects to create immersive environment
- 16. Use of technologyin flipping slides • Gestures using Kinect • Voice commands • Using smartphones • Gesture with touch • Gesture without touching the screen
- 17. Advanced presentation techniques •Using robots to teach language/subjects to the students • Synchronized interactive whiteboards for distance learning • Large-scale immersive environments
- 18. Main content presentationtools Hardware Purpose Interactive white boards [25] [24] Distance learning TV [70] [5] To explain complicated concepts using videos Projectors [71] [4] To view slides and power point presentations Multiple projectors/ large displays [16] Immersive environment Kinnect sensors [72] [1] Gesture based control Robots[76] [77] [75] Predefined discussions, interactivelearning Smartphones [74] Controlling slides andprojector
- 19.
- 20. When and whomto deliver the content? • Context aware smart classroom: detects student location, use bluetooth • Deliver the content according to class schedule • Notification system when instructor made changes in the material
- 21. Video indexing tonavigate through video content • Extracting text using OCR and using it for indexing • Indexing based on instructor provided keywords • Using deep learning methods
- 22. Presenting/distributing content tovery large number of students • Two scenarios based on time and space • Synchronous classroom (Distance learning) – Students listen to lecture at same time but at different locations • Asynchronous classroom (eLearning) – Students listen to lecture at different time and different locations
- 23. Synchronous classrooms (1/2) •Instructor and students are physically apart but connected by a communication channel • Both Iarticipate in the learning process simultaneously, i.e., the students attend lectures from any location but the same time
- 24. Synchronous classrooms (2/2) •Instructor activities (Audio, video, slides, etc.) are captured and streamed to various remote locations in real time • Instructor usually see students on remote sites using display at local site • Interactive whiteboard display the content of whiteboard at remote sites
- 25. Asynchronous classrooms (1/2) Lectureis captured and recorded, uploaded on cloud. Students access the stored lecture using internet at any location any time Classroom Cloud 2+2= 4 2+2=4 Online/ Internet Capture/ Upload
- 26. Asynchronous classrooms (2/2) •Storing and retrieving video from internet is challenging due to large size • Video compression is used to reduce size • Web technologies developed for efficient access • Automatic indexing of Videos
- 27. Challenges and recommendations •Multimodel summary – Efficient recording of instructor’s video, slides, and board work, class interactions as single format • Content conversion – For example, slides to audio or text, transcript in different languages • Interactive content
- 28.
- 29. Engagement measurement and enhancement •Student should be engaged throughout lecture for effective learning • Engaged students => attentive, receptive • Two important steps of smart engagement – Engagement measurement – Enhance engagement
- 30. Factors affecting engagement •Lecture delivery style • Interaction between teacher and student • Interaction among students • Classroom activities – Quiz, competitions, etc. • Group discussions A good instructor adapts the teaching style according to the current engagement level of students!
- 31. Engagement modeling parametersand automatic measurement Parameters Sensor Output Fighting [21] PIR sensor and camera Motion existence and level Noise [21] Microphone Noise existence Sound level [21] Sound sensors Sound level Instructor’s motion [11] Accelerometer Instructor’s motion Closed eyes [106] PTZ cameras Eye detection Head nod [107][12] Camera Face detection Attentiveness [108][109] Camera Eye gaze
- 32. Enhancing student-teacher interaction •ActiveClass framework enable students to ask question anonymously and give feedback in real-time • Communication among heterogeneous devices audio visual devices, projectors, laptop, computer requires efficient design of communication gateway • Active classroom tools and techniques enhances the interaction • Flipped classroom where student listen lectures outside classroom and interacts in classroom
- 33. Enhancing student-student interaction •Classtalk collects all student’s responses and instructor display histogram in real-time • Team building and collaboration among students groups using electronic devices • M-learning solutions increase interaction among students • Various software platforms enhance student interaction using Wikis, groups, forums, polls
- 34. Enhancing student contentinteraction • Students can directly interacts with the content using technology like – Augmented reality – Immersive environment • Such interaction improves the understanding of difficult concepts
- 35. Main engagement enhancementmethods Engagement Aspect Works Hardware Used Tasks Student-teacher interaction ActiveClass [110] Mobile devices Anonymous questions/feedback Bargaoui et al. [82] Tablets, laptops, and projectors Communication getaway for interaction Liu et al. [16] Projectors, tablets Questions usingtablet Tai et al. [104] Interactive whiteboard, a projector, a laptop, and remote control Response through remote control Student-student interaction Abut and Ozturk [70] Desktop, projector, mobile electronicdevices Team building, collaboration Yau et al. [7] PDAs (mobile devices) Project group collaboration Bargaoui et al. [82] Tablets, laptops, and projectors Collaboration withother students Ha and Kim [30] Mobile phones, desktops Interactions with microblogging Ishida [111] Anydevice withdisplay Collaboration among multi-language students Student-content interaction Classtalk [5] Desktop, palmtop, projector, communication network Question/response presentation Kaufmann et al. [112] AR equipment Collaboration, 3D concept presentation Liu et al. [16] Projectors, tablets Immersive environment
- 36. Challenges and recommendations •Engagement and distractions – Equipments used to measure engagement may cause distraction • Interactions in distance learning – Robustness and reliability needs improvement • Multi-model feedback – Non visual methods like Haptics can be used to enhance student engagement
- 37.
- 38. Smart assessment tools •For assessing and evaluating students • For automatic attendance of students • For assessing teaching quality • Plagiarism check
- 39. Automatic student assessment Submissiontype Description Challenges Objective questions (Multiple type questions, fill in the blanks, single numerical values) OMR sheets, computer based evalutation (Moodle, Google Forms, etc.) Limited coverage Programming assignments Careful drafting of input and output formats (HackerRank account, CodeChef, etc.) Partial marking not possible if program is not compiling Essays AI based techniques Only a few aspects can be evaluated automatically Drawings Machine learning based methods Need a trained model, not possible in all cases
- 40. Plagiarism check • Veryhard to automatically check handwritten assignments • A number of tools for plagiarism check in online submissions • Text plagiarism [134]: Turnitin, Urkund, Copycatch, Wcopyfind, GPSP, Ithenticate • Code plagiarism [134]: MOSS, Jplag, Plagiarism-Finder, Plaggie, SIM,
- 41. Smart attendance tools •Traditional roll call is time consuming • Additional smartcards for automatic attendance –NFC, RFID, BLE etc. • Smartphones for attendance –May cause distraction • Biometric systems –Fingerprinting, face recognition, etc.
- 42. Automatic attendance Method Infrastructurerequired Challenges Traditional roll call None Slow, difficult to use, error prone Smartcard NFC/RFID device with student details Device may be lost, forgotten Smartphone Smartphone with Bluetooth low energy sensors/NFC/WIFI/GPS sensors Require smartphone with all the students Biometric Fingerprint reader or cameras for face recognition Accurate but costly
- 43. Automatic teaching assessment •Traditional method: formal feedback at end of semester – Less effective, subjective and biased • By monitoring student activities – As done for student engagement • Student engagement level are also reflection of teaching quality ALPS 1.0 is out attempt to automatically measure teaching quality!
- 44. Challenges and recommendations •Grading detailed questions – Coverage of automatic grading is limited • Universal plagiarism check • Detecting idea level plagiarism • Robust and efficient attendance system • Individual student feedback
- 45.
- 46. Smart physical environment •Conducting classes in open air is distracting and inconvenient in most cases • Confined places have challenges as well as benefits – Challenges: with more people, the air quality may degrade, walls can cause echo – Benefits: it is possible to control temperature, humidity, and air quality within walls of classroom
- 47. Smart physical environment(1/2) • Tools and techniques that maintain physical environment conductive for teaching – Measurement of air quality and reporting – Temperature measurement and control – Sound level and echo cancellation – Automatic light control – Sensing gases and automated ventilation system
- 48. Smart physical environment(2/2) Sensors Processor Actuator Adaptive Algorithms Environmental Standards Mechanical Movement Software Control Alarm Classroom
- 49. Physical environment qualitymeasures Important Environmental aspects Desired Control mechanism Temperature 24◦ to 26◦ Celsius [161] Any modern air conditioning sys- tem is able to control air temper- ature Humidity 40-60% [162] GSM-based system [163], Humid- ity dehumidifier[164],Ventila- tion [165]etc. Radiation none(except sunlight) Sealing the radiation sources or the classrooms Volatile Organic Compounds (VOC) below 200g/m3 [166] Mainly ventilation systems [167] and source management Nitrogen Dioxide(NO2) -from burning fuelitems annual average below below 0.02ppm, hourly average below 0.11 ppm [168] Mainly ventilation systems [167] and source management Carbon Dioxide (CO2) - mainly emitted by humans below 800ppm[169] Mainly ventilation systems [167] and source management Airborne particles - coarse dust particles (PM10) and fineparticles (PM2.5) PM10 below 150 ug/m3 daily average, PM2.5 below 35 ug/m3daily average [170] Mainly ventilation systems [167] and source management Carbon Monoxide(CO) below 25ppm per hour [171] Mainly ventilation systems [167] and source management Sound level 25dB above noise level [172] Automatic noisemeasurement and audio volumecontrol Audio noiselevel below 49.6 dB [173] Sound insulation Lighting 15003000Lux [173] Automatic light control [174]
- 50. Challenges and recommendation •Student diversity – Maintaining environment suitable for diverse students • Control mechanism – Fine level of control is required • Adaptive physical environment – Based on comfort level of occupants
- 51. Summary • Four facets: content, engagement, assessment and environment of a smart classroom is inter-related and overlapping • Researchers works on point problems – Requires integration to other solutions to form one comprehensive solution – Without integration solution is costly • Multiple hardware requirement for each solution • Require to maintain multiple software • Unified and cost effective framework is required • Standardization is required to integrate future solutions in unified framework
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Classtalk: A classroom communication system for active learning. Journal of computing in higher education, 7 (2):3–47, 1996. 6 Takahiro Tagawa, Naomi Fujimura, Satoshi Hasikura, and Hitoshi Inoue. Introduction and manage- ment of inter-campus learning assistant system for distributed campus. In Proceedings of the 37thannual ACM SIGUCCS fall conference: communication and collaboration, pages 253–256. ACM, 2009. 7 Stephen S Yau, Sandeep KS Gupta, Fariaz Karim, Sheikh I Ahamed, Yu Wang, and Bin Wang. Smart classroom: Enhancing collaborative learning using pervasive computing technology. In ASEE Annual Conference and Exposition, pages 13633–13642. sn, 2003. 8 Rebecca Ferguson. Learning analytics: drivers, developments and challenges. International Journal of Technology Enhanced Learning, 4(5/6):304–317, 2012. 9 Cristobal Romero and Sebastian Ventura. Data mining in education. 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