Web-based Board Game for Learning Python

Author(s):  
Li-Wen Huang ◽  
Po-Hsun Cheng ◽  
Li-Wei Chen
Keyword(s):  
2008 ◽  
Vol 26 (4) ◽  
pp. 663-681 ◽  
Author(s):  
Karen Markey ◽  
Fritz Swanson ◽  
Andrea Jenkins ◽  
Brian J. Jennings ◽  
Beth St. Jean ◽  
...  

2020 ◽  
Vol 17 (2) ◽  
pp. 106-113
Author(s):  
Choongmeong Lee ◽  
Sujin Bae ◽  
Jae Jun Nam ◽  
Jae Chan Jin ◽  
Doug Hyun Han

Objective Our previous study suggested that monitoring online board gamers may be an efficient approach to curb illegal gambling. We aimed to invent and validate a behavioral scale for assessing the risk of problematic web-based board gaming.Methods The sample included 300 Korean adults, representing a response rate of 3.1%. All participants were asked to complete a set of questionnaires, which included questions on demographic variables, patterns of online board gaming, and the web-based board game scale score. Exploratory factor analysis was performed to determine whether the items on the new behavioral scale would indicate a risk of pathologic web-based board gaming behavior.Results The internal consistency of the 17-item scale was high (Cronbach’s α=0.89). The test-retest reliability of the 17-item scale in a randomly selected sample of 100 participants in 2 weeks was r=0.77 (p<0.001). The criterion-related validity based on a comparison of the total behavioral scale scores between the high-risk group and low-risk group was relatively high. The data obtained from the 300 participants were acceptable for a factor analysis. After removing 7 items from the 17-item scale, internal consistency (Cronbach’s α) of the 10-item scale increased to 0.936.Conclusion These results showed that the 10-item version of the scale appeared to be more valid than the 17-item version. We suggest that the 10-item web-based board game behavioral scale is a useful tool for assessing the risk of pathologic web-based board gaming.


2008 ◽  
Vol 14 (9/10) ◽  
Author(s):  
Karen Markey ◽  
Fritz Swanson ◽  
Andrea Jenkins ◽  
Brian J. Jennings ◽  
Beth St. Jean ◽  
...  

1998 ◽  
Vol 62 (9) ◽  
pp. 671-674
Author(s):  
JF Chaves ◽  
JA Chaves ◽  
MS Lantz
Keyword(s):  

2013 ◽  
Vol 23 (3) ◽  
pp. 82-87 ◽  
Author(s):  
Eva van Leer

Mobile tools are increasingly available to help individuals monitor their progress toward health behavior goals. Commonly known commercial products for health and fitness self-monitoring include wearable devices such as the Fitbit© and Nike + Pedometer© that work independently or in conjunction with mobile platforms (e.g., smartphones, media players) as well as web-based interfaces. These tools track and graph exercise behavior, provide motivational messages, offer health-related information, and allow users to share their accomplishments via social media. Approximately 2 million software programs or “apps” have been designed for mobile platforms (Pure Oxygen Mobile, 2013), many of which are health-related. The development of mobile health devices and applications is advancing so quickly that the Food and Drug Administration issued a Guidance statement with the purpose of defining mobile medical applications and describing a tailored approach to their regulation.


2008 ◽  
Vol 41 (8) ◽  
pp. 23
Author(s):  
MITCHEL L. ZOLER
Keyword(s):  

2009 ◽  
Vol 42 (19) ◽  
pp. 27
Author(s):  
BRUCE JANCIN
Keyword(s):  

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