Utilising problem-solving: from self-assessment to self regulating

被引:2
作者
Alzaid, Mohammed [1 ,2 ]
Hsiao, Sharon [1 ]
机构
[1] Arizona State Univ, Sch Comp, Informat & Decis Syst Engn, Tempe, AZ USA
[2] Arizona State Univ, Sch Comp,CONTACT,Mohammed Alzaid, Informat & Decis Syst Engn, Mill Ave, Tempe, AZ USA
关键词
Problem-solving; programming learning; self-assessment; self-regulating; NAVIGATION SUPPORT; STUDENT;
D O I
10.1080/13614568.2019.1705922
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
With the wide adoption of online platforms in education, having the content organised and readily available for the learners to self-assess their progress is crucial to ensure academic success. However, without the support of an active mentor, it might be difficult for the learners to guide themselves on how to accurately evaluate their learning outcome. This article focuses on how students benefited from self-assessing their programming course progress using an adaptive platform, namely QuizIT. We discuss the self-assessment procedure and development as it progresses into an open student model learning platform. The initial release was designated to assist programming novices as they encounter the programming learning journey. It was then followed by an extension to open the possibilities to the learner by having access to the learning model and gaining more control over the assessment experience. Quiz Adapt, was the set of features that provided an adaptive recommender based on an open learner model. The data collected from four study setups are evaluated to show the impact on the learning gain from interacting with the system. In addition, this paper sheds light on the effectiveness of daily learning opportunities in the introductory phase of education.
引用
收藏
页码:222 / 244
页数:23
相关论文
共 30 条
[1]  
Alzaid M, 2018, PROC FRONT EDUC CONF
[2]  
Alzaid M, 2017, PROC FRONT EDUC CONF
[3]  
[Anonymous], 2007, International Journal of Artificial Intelligence in Education, DOI 10.1007/3-540-47952-X_31
[4]   Targeting At-risk Students Using Engagement and Effort Predictors in an Introductory Computer Programming Course [J].
Azcona, David ;
Smeaton, Alan F. .
DATA DRIVEN APPROACHES IN DIGITAL EDUCATION, 2017, 10474 :361-366
[5]  
Baker R.S., 2014, LEARNING ANAL RES PR, P61, DOI [DOI 10.1007/978-1-4614-3305-7_4, 10.1007/978-1-4614-3305-7_4]
[6]   The Value of Social: Comparing Open Student Modeling and Open Social Student Modeling [J].
Brusilovsky, Peter ;
Somyurek, Sibel ;
Guerra, Julio ;
Hosseini, Roya ;
Zadorozhny, Vladimir .
USER MODELING, ADAPTATION AND PERSONALIZATION, 2015, 9146 :44-55
[7]  
Brusilovsky P, 2011, LECT NOTES COMPUT SC, V6964, P71, DOI 10.1007/978-3-642-23985-4_7
[8]  
Bull S, 2004, PLANNING, V29, P1
[9]   FEEDBACK AND SELF-REGULATED LEARNING - A THEORETICAL SYNTHESIS [J].
BUTLER, DL ;
WINNE, PH .
REVIEW OF EDUCATIONAL RESEARCH, 1995, 65 (03) :245-281
[10]  
Dimitrova V., 2003, International Journal of Artificial Intelligence in Education, V13, P35