DSLE: Smart Learning Environment (SLE)

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	SLE	CULE	ALE
Detects and takes into account the real-world contexts	Yes	Yes	No
Situates learners in real-world scenarios	Yes	Yes	No
Adapts learning content for individual learners	Yes	No	Yes
Adapts the learning interface for individual learners	Yes	No	Yes
Adapts learning tasks or objectives for individual learners	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
Considers the online learning status of learners	Yes	No	Yes
Considers the real-world learning status of learners	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.

Koper, R. Conditions for effective smart learning environments. Smart Learning Environments, 1:5, 2014.

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).

Última modificación: sábado, 8 de febrero de 2020, 06:22