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MEANINGFUL LEARNING IN U-LEARNING ENVIRONMENTS:
An Experience in Vocational Education
J_. BRITO, L. SEIXAS, I. M. FILHO, A. GOMES, B. MONTEIRO
CENTRO DE INFORMÁTICA, UFPE
AGENDA
• I. Ubiquitous learning
• I.A. Meaningful ubiquitous learning
• II. Method
• II.A. Effectiveness evaluation
• III. Results
• IV. Future works
• IV.A. Heritage education
I. UBIQUITOUS LEARNING
Definition and infrastructure
UBIQUITOUS LEARNING PLATFORM
DE SOUSA MONTEIRO, Bruno; GOMES, Alex Sandro; NETO, Francisco Milton Mendes. Youubi: Open software for ubiquitous learning. Computers in Human Behavior, v. 55, p.
1145-1164, 2016.
ELEMEN TA RY EN T IT I ES
U-LEARNING REFERENCE ARCHITECTURE
DE SOUSA MONTEIRO, Bruno; GOMES, Alex Sandro; NETO, Francisco Milton Mendes. Youubi: Open software for ubiquitous learning. Computers in Human Behavior, 2014.
LOCATION
• All elements that coordinate events are localized and can be monitored
CREATE CHALLENGES
• These are presented to people and get people to go to certain places to solve the challenges.
RECOMMENDATION
• Counted and content suggestions are sent to users based on their profile data and the context in which they are located.
9
COMMUNICATION • The Application has a channel for real-time communication
GAMIFICATION
• As the user solves the challenges and interacts through the system and gains points in numerous dimensions
11
I.A. MEANINGFUL UBIQUITOUS LEARNING
Source: adapted from Huang et al. (2011).
Chang and Z.M. Yeh (2014) reinforce that apprentices do not learn fromtechnology; However, technologies can support productive thinking and favorthe construction of meanings.
Source: adapted from Huang et al. (2011).
EVOLUTIONARY FRAMEWORK MODEL FOR ASSESSMENT OF
UBIQUITOUS LEARNING
II. METHOD
CONTEXT AND PARTICIPANTS
• Context: Federal Institute of Technological Education
• Course: Technical Course in Computer Science and Chemistry
• Discipline: Geography, total workload 60 (sixty) hours
• Participants: 38 apprentices.
II.B. EFFECTIVENESS EVALUATION
UBIQUITOUS ACTIVITIES
UBIQUITOUS LEARNING DATA ANALYSIS
DATA CLASSIFICATION
Category Criteria
1 She/he presents a key concept related to the subject.
2 She/he presents and discusses a key concept related to the subject.
3 She/he relates the context to the local context.
4 She/he presents a problem.
5 She/he presents something simple / superficial related to the subject.
Source: adapted from Huang et al. (2011).
III. RESULTS
SECOND WEEK
The total of 28 apprentices interacted in 08 valid challenges created with a total of283 attempts to correct answers, 223 correct answers and 60 wrong answers on thetheme of urbanization.
Second week: answer to challenges
FORTH WEEK
40 valid challenges were created with a total of 647 responses, of which 375 correctanswers and 272 wrong answers on the theme of urbanization, with a greateremphasis on Urban Social Problems, Urban Agglomeration, Traffic Disruption, SpaceSegregation and Urban Mobility.
Forth week: answer to challenges
Pre-test and post-test comparative results
Comparison of paired means (Wilcox test)
The null hypothesis was rejected at a 95% confidence level.
IV. FUTURE WORKS
Heritage education
“This analytic orientation inspires four key shifts in our approach to HCI4D efforts: generative models
of culture, development a historical program, uneven economic and cultural epistemologies.”
IRANI, Lilly et al. Postcolonial computing: a lens on design and development. In: Proceedings of the SIGCHI conference on human factors in computing systems. ACM, 2010. p. 1311-1320.
ETHNOGRAPHIC DESIGN
• (…) participants and ethnographers make knowledge and places together as they tour the home. This enables the sensory ethnographer to focus on how the place(s) she/he seeks to understand are constituted both in the experience of participants and in the ethnographic descriptions she or he ultimately creates. (p. 25:6)
PINK, Sarah et al. Applying the lens of sensory ethnography to sustainable HCI. ACM Transactions on Computer-Human Interaction (TOCHI), v. 20, n. 4, p. 25, 2013.
OBJECTIVE
Promote Heritage Education using a ubiquitouslearning platform
• People know historical places from the point of view ofother people, using their memories
• People can know the city using routes suggested byanother users
• People reflect about their own identity in relation thethe place they live
• People learn curiosities about the historical placesthrough challenges
MEANINGFUL LEARNING IN U-LEARNING ENVIRONMENTS:
An Experience in Vocational Education
J_. BRITO, L. SEIXAS, I. M. FILHO, A. GOMES, B. MONTEIRO
CENTRO DE INFORMÁTICA, UFPE
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