Visionary: a framework for analysis and visualization of provenance data

Oliveira, Weiner de; Braga, Regina; David, José; Ströele, Victor; Campos, Fernanda; Castro, Gabriellla

doi:10.1007/s10115-021-01645-6

Cited by 8 publications

(1 citation statement)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The effectiveness of teacher-student interaction directly affects the effect of classroom teaching [1][2][3][4][5][6][7][8]. The visualization of text, image, and video data has been widely applied in various industries and social groups, and can be introduced to the teaching of design courses [9][10][11][12][13][14][15]. Compared with traditional offline classroom, the synchronous online classroom of digital art design (DAD) involves highly complex teacherstudent interactions.…”

Section: Introductionmentioning

confidence: 99%

Feasibility Analysis of Visual Interaction Mode in Digital Art Design Teaching

Zhang¹

2022

Int. J. Emerg. Technol. Learn.

View full text Add to dashboard Cite

The visual presentation of teaching contents facilitates students to master the relevant knowledge and skills of digital art design (DAD). The existing studies focus on the construction of traditional classroom teaching and interaction strategies, but rarely tackle the visual interaction (VI) technology of synchronous online classroom. This paper analyzes the feasibility of VI mode in DAD teaching. Firstly, the authors detailed the realization steps for teacher-student VI in the DAD teaching process control platform, and constructed the topology of VI system in DAD teaching. After a data analysis on VI flow, the authors parsed the VI flow data, and encoded the teacher-student VI involved in DAD teaching. Based on the improved neural network, a model was established to predict the degree of realization for course goals of DAD teaching. Through experiments, the teacher-student VI in DAD teaching was described statistically, and the relevant analysis results were obtained, which verify the effectiveness of our prediction model.

show abstract