Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3122
Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
Persuasive technology can support users of self-paced eLearning courses during critical moments of low motivation. Agent-based models (ABMs) - a relatively unfamiliar phenomenon within the persuasive technology and eLearning domains- offers a potentially relevant methodology to understand when the support should be delivered. Using ABMs, the dynamics of motivational user states can be simulated. Subsequently, emerging user patterns can be traced that can potentially provide insight in the ebb and flow of motivation. For the purpose of this study, we designed an exploratory ABM on motivation based on the mental energy notion of which the foundations can be found both within the literature of motivational psychology and agent-based modeling. During the simulations we succeeded in generating moments of critically low user motivation. In addition, we were able to simulate the positive impact of external user support at those critical moments. These results suggest that it is plausible to put further energy in developing ABM models with the ultimate goal of feeding persuasive technology with the ability to deliver just-in-time user support during eLearning.
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