scholarly journals A Collaborative Learning Framework for Computational Thinking Development through Game Programming

Author(s):  
Ângelo Magno de JESUS ◽  
Ismar Frango SILVEIRA
Author(s):  
Angelo Magno De Jesus ◽  
Ismar Frango Silveira

Computational Thinking (CT) can amplify learners’ skill sets so that they become excellent problem-solvers. Game-Based Learning and Collaborative Learning are two approaches that may aid in the development of CT skills. This paper describes a framework based on Game and Problem-Based Learning Strategies which aims to enhance the CT teaching and improves students’ social skills, considering aspects of fun. The framework stands out for including collaborative learning features defined in the main literature. Also, the strategy was developed specifically to fit the games’ dynamics. The approach was evaluated via metacognitive and transactive analysis and by a survey. The results showed evidence that the method is able to stimulate interaction among students to apply problem-solving strategies.


2021 ◽  
Vol 8 ◽  
pp. 238212052110003
Author(s):  
Aida J Azar ◽  
Amar Hassan Khamis ◽  
Nerissa Naidoo ◽  
Marjam Lindsbro ◽  
Juliana Helena Boukhaled ◽  
...  

Background: The COVID-19 pandemic has forced medical schools to suspend on-campus live-sessions and shift to distance-learning (DL). This precipitous shift presented medical educators with a challenge, ‘to create a “ simulacrum” of the learning environment that students experience in classroom, in DL’. This requires the design of an adaptable and versatile DL-framework bearing in mind the theoretical underpinnings associated with DL. Additionally, effectiveness of such a DL-framework in content-delivery followed by its evaluation at the user-level, and in cognitive development needs to be pursued such that medical educators can be convinced to effectively adopt the framework in a competency-based medical programme. Main: In this study, we define a DL-framework that provides a ‘ simulacrum’ of classroom experience. The framework’s blueprint was designed amalgamating principles of: Garrison’s community inquiry, Siemens’ connectivism and Harasim’s online-collaborative-learning; and improved using Anderson’s DL-model. Effectiveness of the DL-framework in course delivery was demonstrated using the exemplar of fundamentals in epidemiology and biostatistics (FEB) course during COVID-19 lockdown. Virtual live-sessions integrated in the framework employed a blended-approach informed by instructional-design strategies of Gagne and Peyton. The efficiency of the framework was evaluated using first 2 levels of Kirkpatrick’s framework. Of 60 students, 51 (85%) responded to the survey assessing perception towards DL (Kirkpatrick’s Level 1). The survey-items, validated using exploratory factor analysis, were classified into 4-categories: computer expertise; DL-flexibility; DL-usefulness; and DL-satisfaction. The overall perception for the 4 categories, highlighted respondents’ overall satisfaction with the framework. Scores for specific survey-items attested that the framework promoted collaborative-learning and student-autonomy. For, Kirkpatrick’s Level 2 that is, cognitive-development, performance in FEB’s summative-assessment of students experiencing DL was compared with students taught using traditional methods. Similar, mean-scores for both groups indicated that shift to DL didn’t have an adverse effect on students’ learning. Conclusion: In conclusion, we present here the design, implementation and evaluation of a DL-framework, which is an efficient pedagogical approach, pertinent for medical schools to adopt (elaborated using Bourdieu’s Theory of Practice) to address students’ learning trajectories during unprecedented times such as that during the COVID-19 pandemia.


2021 ◽  
Vol 30 (3) ◽  
pp. 334
Author(s):  
Lap Kei Lee ◽  
Tsz Kin Cheung ◽  
Lok Tin Ho ◽  
Wai Hang Yiu ◽  
Nga In Wu

Author(s):  
Haimei Zhao ◽  
Wei Bian ◽  
Bo Yuan ◽  
Dacheng Tao

Scene perceiving and understanding tasks including depth estimation, visual odometry (VO) and camera relocalization are fundamental for applications such as autonomous driving, robots and drones. Driven by the power of deep learning, significant progress has been achieved on individual tasks but the rich correlations among the three tasks are largely neglected. In previous studies, VO is generally accurate in local scope yet suffers from drift in long distances. By contrast, camera relocalization performs well in the global sense but lacks local precision. We argue that these two tasks should be strategically combined to leverage the complementary advantages, and be further improved by exploiting the 3D geometric information from depth data, which is also beneficial for depth estimation in turn. Therefore, we present a collaborative learning framework, consisting of DepthNet, LocalPoseNet and GlobalPoseNet with a joint optimization loss to estimate depth, VO and camera localization unitedly. Moreover, the Geometric Attention Guidance Model is introduced to exploit the geometric relevance among three branches during learning. Extensive experiments demonstrate that the joint learning scheme is useful for all tasks and our method outperforms current state-of-the-art techniques in depth estimation and camera relocalization with highly competitive performance in VO.


2022 ◽  
pp. 379-399
Author(s):  
Ieda M. Santos ◽  
Wenli Wu

Online learning continues to grow and is increasing including more diverse students. Diverse students with various backgrounds and experiences challenge educators to implement pedagogies to achieve equitable learning experiences and outcomes. This chapter aims to discuss four equity pedagogies commonly referred to in the literature that can contribute to democratic and inclusive learning experiences for all students. The chapter's four strategies include pedagogic voice, universal design for learning, equitable assessment, and collaborative learning. Although these strategies were discussed separately, the universal design for learning framework can incorporate both the pedagogic voice, equitable assessments, and collaborative learning while considering their unique perspectives. If well-designed and implemented, these strategies can help all students to receive fair education and prepare them to succeed in a changing world and become agents for social change. The chapter includes recommendations for practice and future research.


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