Bibliografía y otros recursos recomendados.
| Site: | Universidad de la República |
| Course: | Rediseñar la Universidad Digital |
| Book: | Bibliografía y otros recursos recomendados. |
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| Date: | Wednesday, 22 January 2025, 2:59 PM |
Description
Bibliografía y otros recursos recomendados.
Table of contents
- 1. Technology Enhanced Learning Environment: TELE
- 2. Adaptive Learning Environment: ALE
- 3. Context-aware, Ubiquitous Learning Environment
- 4. Smart Learning Environment
- 5. Evaluación: concepto y abordajes
- 6. Tecnologías aplicadas al aprendizaje
- 7. Parque -Museo, con códigos QR
- 8. Realidad virtual para enseñar idiomas
- 9. Realidad virtual para cirujía de cerebro
1. Technology Enhanced Learning Environment: TELE
TELE, defined as specialized area that encompasses virtual and physical technology enhanced learning environments (incorporating physical learning spaces, institutional virtual learning environments, personalized learning environments, and mobile and immersive learning environments) … (Sen and Passy, 2013). TELE - use ICT tools to support and facilitate learning. Technology plays significant role in making learning more effective, efficient, or enjoyable. TELE: permit multiple usage of the same learning space for diverse pedagogies learning support tools and technological resources support students to acquire skills and knowledge support teachers to transfer knowledge and learning contents engage students in collaborative learning Characteristics of TELEs Recommendations for classroom implementation of TELE with a student-centered focus Adapted from Rogers (2002) and Shapiro, Roskos, and Cartwright (1995) Classroom design: Be aware of the intended use of instructional spaces. Do you want to encourage small-group learning, large-group learning, or collaborative learning? Pod Design: Plan for ample table space and physical arrangements to encourage small-group work and collaborative learning Essentiality of Connectivity: Ideally, each student in the class would have access to a technology device (i.e. laptop, ePC, tablet, iPad, etc.), as well as have access to a high-speed WiFi Internet network. Technical Support: Provide human resources to initiate and maintain technology-enhanced teaching activities Presentation Equipment: Use of SMART Boards, document cameras, projection equipment to maximize visibility of technology images. Teaching Tools: These include strategies and methods to support teaching and to facilitate learning through technology. Learning Tools: Technology used in the classroom allows for an external outlet for students’ internalized learning tools (such as self-efficacy, internal motivation and predicted success). Examples of TELEs: Click here for the examples of TELE The followings are examples of TELE Physlets - physics applets: http://webphysics.davidson.edu/Applets/DownLoad_Files/intro_download.html Virtual field trip: http://www.learnz.org.nz/what-are-learnz-field-trips Maths blaster: http://www.mathblaster.com/About.aspx Web-based Inquiry Science Environment: https://wise.berkeley.edu/ Others: MOOCs, Scratch, Alice, RoboMind, Logo, Internet scavenger hunt, Moodle, WebQuest, References and further reading: A. Sen and D. Passy, "Globalisation of Next Generation Technology Enhanced Learning Environments (TELE) for Learning: Contexualisations in the Asia-Pacific region," in Proceedings of 5th International Conference on Technology for Education, IEEE, 2013. https://www.researchgate.net/publication/271551281_Globalisation_of_Next_Generation_Technology_Enhanced_Learning_Environment_TELE_for_STEM_Learning_Contexualizations_in_the_Asia-Pacific_Region G. Peter and R. Symeon, "Technology-Enhanced Learning- Design Patterns and Pattern Languages," Technology-Enhanced Learning , vol. 2, 2010. Aleven, V., Stahl, E., Schworm, S., Fischer, F., & Wallace, R. (2003). Help seeking and help design in interactive learning environments. Review of Educational Research, 73(3), 277–320. https://doi.org/10.3102/00346543073003277 Shapiro, W. L., & Roskos, K. (1995). Technology enhanced learning environments. Change, 27(6), 67– 69. Rogers, P.L. (2002) Designing Instruction for Technology-Enhanced Learning. IGI Global, Chapter 1.Elaborated by Prof. Solomon Oyelere as course material in the course DSLE delivered in FIng in 2020
Technology and physical environment
Technology equipment
Technology and instructional strategies
2. Adaptive Learning Environment: ALE
Elaborated by Prof. Solomon Oyelere as course material in the course DSLE, delivered in FIng in 2020
Adaptive learning Environment (ALE)
ALE is also referred as intelligent tutoring system.
1.Support students to acquire knowledge and conduct investigation according to certain learner’s attributes:
- consider different students’ characteristics
- learning status of each student
- personal factors: learning style, preferences, learning progress, affective/emotional state, motivational aspects, knowledge levels and cognitive abilities
2.To provide adaptive and intelligent system capacities to teachers and students, for example, intelligent support, adaptive interfaces, personalized support (Mampadi, Chen, Ghinea, and Chen 2011; Papanikolaou, Grigoriadou, Magoulas, and Kornilakis 2002; Yang et al. 2013).
3.According to Clancey (1984), conventional intelligent tutoring system typically consists of four components:
- an expert model or expert knowledge model that contains the teaching materials,
- a student model or learner model that evaluates students’ learning status and performance,
- an instructional model or pedagogical knowledge model that determines teaching content, educational tools and presentation methods based on the outcomes of the student model,
- a user interface for interacting with students
Examples of adaptation strategies for providing personalized learning in web-based systems Brusilovsky (2001):
- adaptive presentation, which presents personalized learning materials
- adaptive navigation support, guides individual students to browse learning content based on the recommended learning paths.
Further readings to demonstrate several examples of adaptive hypermedia learning systems with experiments to confirm effectiveness, based on the two adaptation strategies: Tseng et al. (2008a,b), Gonzalez and Ingraham (1994), Papanikolaou et al. (2002), Karampiperis and Sampson (2005), Martens (2005).
- Adaptive learning systems which considers different personal factors, Kinshuk et al. 2012, Tseng et al. (2008a,b) and Yang et al. (2013a,b).
- Adaptive learning systems based on mobile, wireless communication and sensing technologies: Hwang et al. (2010), Hsieh et al. (2011)
Current challenge: applying intelligent tutoring or adaptive learning techniques to real-world learning situations.
References and further reading:
WJ Clancey, Methodology For Building An Intelligent Tutoring System, in Methods And Tactics In Cognitive Science, ed. by W Kintsch, PG Polson, JR Miller (Lawrence Erlbaum Associates, Hillsdale, NJ, 1984), pp. 51–84.
F Mampadi, SYH Chen, G Ghinea, MP Chen, Design of adaptive hypermedia learning systems: a cognitive style approach. Comput. Educ. 56(4), 1003–1011 (2011).
KA Papanikolaou, M Grigoriadou, GD Magoulas, H Kornilakis, Towards new forms of knowledge communication: the adaptive dimension of a web-based learning environment. Comput. Educ. 39, 333–360 (2002).
CC Yang, CM Hung, GJ Hwang, SS Tseng, An evaluation of the learning effecttiveness of concept map-based science book reading via mobile devices. Educ. Technol. Soc 16(3), 167–178 (2013).
TC Yang, GJ Hwang, SJH Yang, Development of an adaptive learning system with multiple perspectives based on students’ learning styles and cognitive styles. Educ. Technol. Soc. 16(4), 185–200 (2013).
P Karampiperis, D Sampson, Adaptive learning resources sequencing in educational hypermedia systems. Educ. Technol. Soc 8(4), 128–147 (2005)
Kinshuk, T Lin, User exploration based adaptation in adaptive learning systems. Int. J. Inf. Syst. Educ 1(1), 22–31 (2003)
Kinshuk, NS Chen, S Graf, GJ Hwang, Adaptive Learning Systems, in Knowledge Management, Organizational Intelligence and Learning and Complexity, ed. by UNESCO-EOLSS Joint Commitee (Encyclopedia of Life Support Systems(EOLSS), Developed under the Auspices of the UNESCO, Eolss Publishers, Oxford,UK, 2012).
M Specht, G Weber, S Heitmeyer, V Schöch, AST: Adaptive WWW-Courseware for Statistics, in Proceedings of Workshop “Adaptive Systems and User Modeling on the World Wide Web” at 6th International Conference on User Modeling, June 2-5, 1997, Chia Laguna, Sardinia, ed. by P Brusilovsky, J Fink, J Kay (Italy, 1997), pp. 91–95.
JCR Tseng, HC Chu, GJ Hwang, CC Tsai, Development of an adaptive learning system with two sources of personalization information. Comput. Educ. 51(2), 776–786 (2008).
SS Tseng, JM Su, GJ Hwang, GH Hwang, CC Tsai, CJ Tsai, An object-oriented course framework for developing adaptive learning systems. Educ. Technol. Soc. 11(2), 171–191 (2008).
P Brusilovsky, Adaptive hypermedia. User Model User Adapt Interact 11, 87–110 (2001).
GJ Hwang, FR Kuo, PY Yin, KH Chuang, A heuristic algorithm for planning personalized learning paths for context-aware ubiquitous learning. Comput. Educ. 54(2), 404–415 (2010).
SW Hsieh, YR Jang, GJ Hwang, NS Chen, Effects of teaching and learning styles on students’ reflection levels for ubiquitous learning. Comput. Educ. 57(1), 1194–1201 (2011).
CC Yang, CM Hung, GJ Hwang, SS Tseng, An evaluation of the learning effectiveness of concept map-based science book reading via mobile devices. Educ. Technol. Soc 16(3), 167–178 (2013a).
TC Yang, GJ Hwang, SJH Yang, Development of an adaptive learning system with multiple perspectives based on students’ learning styles and cognitive styles. Educ. Technol. Soc. 16(4), 185–200 (2013b).
A Martens, Modeling Of Adaptive Tutoring Processes, in Web-Based Intelligent e-Learning Systems: Technologies and Applications, ed. by ZM Ma, Chapter 10th edn. (Information Science Publishing, Hershey, London, 2005), pp. 193–215.
AV Gonzalez, LR Ingraham, Automated exercise progression in simulation-based training. IEEE Transactions on System, Man, Cybernetics 24(6), 863–874 (1994).
2.1. Mirada crítica al aprendizaje adaptativo
Ver el blog con el desarrollo del tema aquí:
3. Context-aware, Ubiquitous Learning Environment
Elaborated by Prof. Solomon Oyelere as course material in the course DSLE delivered in FIng in 2020
3. Context-aware Ubiquitous Learning Environment (CULE)
CULE has the abilities to detect the learner’s context and to adapt its behavior accordingly (Gomez and Fabregat, 2010, Hsu et al., 2016; Gomez et al. 2016; Wu et al., 2012). Examples of contextual entities in a learning environment are the learner’s current place, position, time, other nearby learners, learning style, and learning history (Hasanov, and Laine, 2017). Context-aware learning environments can detect the learner’s context and adapt learning materials to match the context. The support for context-awareness is essential in these systems so that they can make learning contextually relevant. It helps to situate students in real-world learning scenarios.
Examples of learning environments that combine real-life contexts and digital-world resources to provide students with direct experiences of the real world with sufficient learning support, Minami et al. 2004; Hung et al. 2014; Wu et al. 2013a,b.
Examples of using mobile, wireless communication and sensing technologies for developing CULE, Ogata and Yano 2004, Hwang et al. 2008; Tsai et al. 2012, Hwang et al. 2012.
CULE detect the real-world status of learners using sensing technologies such as RFID/NFC, GPS, Camera, Microphone, IR (infrared)-based sensors, interact with the learner through wireless networks, present learning guidance, and offer supplementary materials or feedback.
Current challenges: incorporating intelligent tutoring or adaptive learning techniques to context-aware ubiquitous learning
References and Further reading:
PH Hung, GJ Hwang, YF Lin, TH Wu, IH Su, Seamless connection between learning and assessment- applying progressive learning tasks in mobile ecology inquiry. Educ Tech Soc 16(1), 194–205 (2013). https://www.jstor.org/stable/jeductechsoci.16.1.194?seq=1
R Joiner, J Nethercott, R Hull, J Reid, Designing educational experiences using ubiquitous technology. Comput. Hum. Behav. 22(1), 67–76 (2006). https://www.sciencedirect.com/science/article/abs/pii/S0747563205000117
GJ Hwang, CC Tsai, HC Chu, Kinshuk, CY Chen, A context-aware ubiquitous learning approach to conducting scientific inquiry activities in a science park. Australas. J. Educ. Technol. 28(5), 931–947 (2012).
https://ajet.org.au/index.php/AJET/article/view/825
IC Hung, XJ Yang, WC Fang, GJ Hwang, NS Chen, A context-aware video prompt approach to improving in-field reflection levels of students. Comput. Educ. 70(1), 80–91 (2014).
https://www.sciencedirect.com/science/article/abs/pii/S0360131513002297
H Ogata, Y Yano, Context-Aware Support For Computer-Supported Ubiquitous Learning, in Paper presented at the 2nd IEEE International Workshop on Wireless and Mobile Technologies in Education (JhongLi, Taiwan, 2004).
https://ieeexplore.ieee.org/abstract/document/1281330
Y Rogers, S Price, C Randell, DS Fraser, M Weal, G Fitzpatrick, Ubi-learning integrating indoor and outdoor learning experiences. Communications of the ACM 48(1), 55–59 (2005).
HK Wu, SWY Lee, HY Chang, JC Liang, Current status, opportunities and challenges of augmented reality in education. Comput. Educ. 62, 41–49 (2013a)
https://www.sciencedirect.com/science/article/abs/pii/S0360131512002527
PH Wu, GJ Hwang, WH Chai, An expert system-based context-aware ubiquitous learning approach for conducting science learning activities. Educ. Technol. Soc. 16(4), 217–230 (2013b).
HC Chu, GJ Hwang, CC Tsai, A knowledge engineering approach to developing mindtools for context-aware ubiquitous learning. Comput. Educ. 54(1), 289–297 (2010).
GJ Hwang, CC Tsai, SJH Yang, Criteria, strategies and research issues of context-aware ubiquitous learning. Educ. Technol. Society 11(2), 81–91 (2008).
PS Tsai, CC Tsai, GJ Hwang, Developing a survey for assessing preferences in constructivist context-aware ubiquitous learning environments. J. Comp. Assist. Learn. 28(3), 250–264 (2012).
IC Hung, XJ Yang, WC Fang, GJ Hwang, NS Chen, A context-aware video prompt approach to improving in-field reflection levels of students. Comput. Educ. 70(1), 80–91 (2014).
M Minami, H Morikawa, T Aoyama, The Design Of Naming-Based Service Composition System For Ubiquitous Computing Applications, in In the Proceedings of the 2004 International Symposium on Applications and the Internet Workshops (SAINTW’04) (IEEE Computer Society, Washington, DC, 2004), pp. 304–312.
Hsu, T., Chiou, C., Tseng, J. C. R., and Hwang, G. (2016). Development and Evaluation of an Active Learning Support System for Context-Aware Ubiquitous Learning. Learning Technologies, IEEE Transactions on Learning Technologies, 9(1):37–45.
G´omez, S. and Fabregat, R. (2010). Context-Aware Content Adaptation in mLearning. In Proceedings of the 9th World Conference on Mobile and Contextual Learning.
G´omez, S., Zervas, P., Sampson, D. G., and Fabregat, R. (2014). Context-aware adaptive and personalized mobile learning delivery supported by UoLmP. Journal of King Saud University - Computer and Information Sciences, 26(1):47–61.
Wu, P.-H., Hwang, G.-J., Su, L.-H., and Haung, Y. (2012). A Context-Aware Mobile Learning System for Supportive Cognitive Apprenticeships in Nursing Skills Training. Educational Technology & Society, 5(1):223–236.
Gomez, J., Huete, J., and Hernandez, V. (2016). A contextualized system for supporting active learning. IEEE CSEDU 2017 - 9th International Conference on Computer Supported Education Transactions on Learning Technologies, (99):196–202.
Hasanov, A. and Laine, T. A Survey of Context-awareness in Learning Environments in 2010-2016. In Proceedings of the 9th International Conference on Computer Supported Education (CSEDU 2017) - Volume 1, pages 234-241.
4. Smart Learning Environment
Elaborated by Prof. Solomon Oyelere as course material in the course DSLE delivered in FIng in 2020
4. Smart Learning Environment (SLE)
SLE is a new concept of learning in the digital age. In fact, Zhu et al. (2016, p. 3), “there is not a clear and unified definition of smart learning so far. Multidisciplinary researchers and educational professionals are continuously discussing the concept”.
SLE create adaptations for appropriate learning, provide appropriate support such as feedback, suggestions, help, guidance for individual learner’s requirements, at the right time, and right places. The individual learner’s requirements are determined by analyzing learning styles, learning performance, learning behaviors, and the situated learning contexts. In SLEs, the learner’s exploit smart devices and intelligent technologies via wireless network to access digital resources and engage in personalized and seamless learning.
SLE combines the features of TELEs, ALEs and CULEs by creating a hybrid system with advanced support for teachers and students. The goals of SLEs are:
to offer personalized, timely, accurate, seamless, rich, and supportive learning experience in formal and informal learning scenarios.
Using learning analytics, SLE could deliver accurate and rich learning services
Characteristic features and the potential criteria of smart learning environment
Ten key features of smart learning environments according to Zhu et al. (2016, pp. 11):
1. Location-Aware: Sense learner’s location in real time;
2. Context-Aware: Explore different scenarios and information of activity;
3. Socially Aware: Sense social relationship;
4. Interoperability: Set standard between different resource, service and platform;
5. Seamless Connection: Provide continuous service when any device connects;
6. Adaptability: Push learning resource according to learning access, preference and
demand;
7. Ubiquitous: Predict learner’s demand until express clearly, provide visual and
transparent way to access learning resource and service to learner;
8. Whole Record: Record learning path data to mine and analyze deeply, then give
reasonable assessment, suggestion and push on-demand service;
9. Natural Interaction: Transfer the senses of multimodal interaction including
position and facial expression recognition;
10. High Engagement: Immersing in multidirectional interaction learning experience in technology-riched environment.
According to Hwang (2014), the potential criteria of SLEs are context-aware, can offer instant and adaptive support, can adapt the user interface and the subject contents to meet the personal factors, and learning status of individual learners.
Hwang, (2014) Comparisons of smart learning, context-aware u-learning systems and adaptive learning
Features | CULE | ALE | |
Detects and takes into account the real-world contexts | Yes | Yes | No |
Yes | Yes | No | |
Yes | No | Yes | |
Yes | No | Yes | |
Yes | No | No | |
Provides personalized feedback or guidance | Yes | Yes | Yes |
Provides learning guidance or support across disciplines | Yes | No | No |
Provides learning guidance or support across contexts (e.g., in classrooms, on school campuses, in the library, and on the street) | Yes | Yes | No |
Recommends learning tools or strategies | Yes | No | No |
Yes | No | Yes | |
Yes | Yes | No | |
Facilitates both formal and informal learning | Yes | Yes | No |
Takes multiple personal factors and environmental factors (e.g., learning needs, preferences, schedules and real-world contexts) into account | Yes | No | No |
Interacts with users via multiple channels (e.g., smartphones, Google Glass, or other ubiquitous computing devices) | Yes | Yes | No |
Provides support to learners with “in advance adaptation” across real and virtual contexts | Yes | No | No |
Provides support to learners with “on the run adaptation” across real and virtual contexts | Yes | No | No |
Current challenges: contextualising different scenarios, modules, lessons, learning content, learning objects, and environments to possess smart features.
References and Further reading:
Zhu, Z.-T., Yu, M.-H., Riezebos, P. (2016). A research framework of smart education. Smart Learning Environment vol. 3, no. 1, pp. 1–17.
https://www.researchgate.net/publication/299520194_A_research_framework_of_smart_education
Koper, R. Conditions for effective smart learning environments. Smart Learning Environments, 1:5, 2014.
https://slejournal.springeropen.com/articles/10.1186/s40561-014-0005-4
Hwang, G-J. Definition, framework and research issues of smart learning environments - a context-aware ubiquitous learning perspective. Smart Learning Environments, 2014, 1:4.
S. Kinshuk, Graf, Ubiquitous Learning. Springer Press, Berlin Heidelberg New York, 2012.
J.M. Spector, Conceptualizing the emerging field of smart learning environments. Smart Learning Environments 1(1), 1–10 (2014).
K.S. Noh, S.H. Ju, J.T. Jung, An exploratory study on concept and realization conditions of smart learning. J. Digit. Convergence 9(2), 79–88 (2011).
Z.T. Zhu, B. He, Smart Education: new frontier of educational informatization. E-education Research 12, 1–13 (2012).
Z.T. Zhu, D.M. Shen, Learning analytics: the science power of smart education. E-education Research 5, 5–12 (2013).
R. Huang, J. Yang, Y. Hu, From digital to smart: the evolution and trends of learning environment. Open Educ. Res. 1, 75–84 (2012).
I.A Essa, Ubiquitous sensing for smart and aware environments. Personal Communications, IEEE 7(5), 47–49 (2000).
HK Wu, SWY Lee, HY Chang, JC Liang, Current status, opportunities and challenges of augmented reality in education. Comput. Educ. 62, 41–49 (2013).
GJ Hwang, CC Tsai, SJH Yang, Criteria, strategies and research issues of context-aware ubiquitous learning. Education Technology Society 11(2), 81–91 (2008).
5. Evaluación: concepto y abordajes
Este mapa mental aporta muchos elementos para tomar decisiones acerca del proceso de evaluación.
Aquí lo pueden ver en grande
5.1. Evaluación online en educación superior
Artículo muy recomendable que aborda el tema en varias dimensiones y da ideas prácticas:
García-Peñalvo, F. J., Abella-García, V., Corell, A., & Grande, M. (2020). La evaluación online en la educación superior en tiempos de la COVID-19.
Disponible en: https://repositorio.grial.eu/bitstream/grial/2010/1/a12.pdf
Libro escrito por docentes de UDELAR sobre evaluación
Rodriguez, C. , Czerwonogora, A., Verde, J., & Doninalli, M. EVALUACIÓN FORMATIVA Y HERRAMIENTAS TECNOLÓGICAS.Descargar aquí: https://udelar.edu.uy/eduper/wp-content/uploads/sites/29/2015/09/evaluacion.pdf
6. Tecnologías aplicadas al aprendizaje
Hay diversos tipos de tecnologías que se pueden utilizar para expandir, enriquecer y dar movilidad y autonomía a los entornos y a los procesos de aprendizaje.
Comparto este video que habla de algunas tecnologías emergentes y las experiencias de aplicarlas a la enseñanza.
https://tophat.com/blog/education-technology-trends/
7. Parque -Museo, con códigos QR
8. Realidad virtual para enseñar idiomas
9. Realidad virtual para cirujía de cerebro
Este video muestra RV aplicada a la cirugía de cerebro. No es para propósito educativo, pero veamos qué interesantes posibilidades surgen para la enseñanza de la medicina, con esta tecnología.