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DTSTAMP:20181221T160727Z
LOCATION:D222
DTSTART;TZID=America/Chicago:20181112T111000
DTEND;TZID=America/Chicago:20181112T113000
UID:submissions.supercomputing.org_SC18_sess166_ws_bphpcte111@linklings.co
 m
SUMMARY:Evaluating Active Learning Approaches for Teaching Intermediate Pr
 ograming at an Early Undergraduate Level
DESCRIPTION:Workshop\nEducation, Scientific Computing, Training, Workshop 
 Reg Pass, Scalable and Sustainable Approaches for HPC Training and Educati
 on\n\nEvaluating Active Learning Approaches for Teaching Intermediate Prog
 raming at an Early Undergraduate Level\n\nChakravorty, McMullen, Liu, Ghaf
 fari, Rodriguez...\n\nThere is a growing need to provide intermediate prog
 raming classes to STEM students early in their undergraduate careers. Thes
 e efforts face significant challenges owing to the varied computing skill-
 sets of learners, requirements of degree programs and the absence of a com
 mon programing standard. Instructional scaffolding and active learning met
 hods using Python offer avenues to support these students with varied need
 s. Here, we report on quantitative and qualitative outcomes from three dis
 tinct models of programing education that (i) connect coding to hands-on “
 maker” activities; (ii) incremental learning of computational thinking ele
 ments through guided exercises using Jupyter Notebooks; and (iii) problem-
 based learning with step-wise code fragments leading to algorithmic implem
 entation.  Performance in in-class activities, capstone projects, in-perso
 n interviews and extensive surveys informed us about the effectiveness of 
 these approaches on various aspects of student learning.  Students with pr
 evious coding experience were able to rely on broader skills and grasp con
 cepts faster than students who recently attended an introductory programin
 g session. We find that while maker-space activities were engaging and exp
 lained basic programing concepts, they lost their appeal in complex progra
 ming scenarios. Students grasped coding concepts fastest using the Jupyter
  notebooks, while the problem-based learning approach was best at having s
 tudents understand the core problem and create inventive means to address 
 them.
URL:https://sc18.supercomputing.org/presentation/?id=ws_bphpcte111&sess=se
 ss166
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