Apogee

Fu, Xiang; Peltsverger, Boris; Qian, Kai; Tao, Lixin; Liu, Jigang

doi:10.1145/1352322.1352163

Cited by 12 publications

(3 citation statements)

References 5 publications

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“…Early experiences about computer-based assessment using Ceilidh were described in 1993 by Benford et al [6]. Since then, many other similar tools have been developed to assist the teacher in programming practices for diverse languages, such as ASSYST for Ada [23], CAP for Pascal programs [41], Scheme-robo for the functional language scheme [40], LinuxGym to test scripts [42], APOGEE for web-based computing [15], or JavaMarker for Java [1]. Ceilidh, later renamed CourseMarker [18] was also redesigned to support new languages like Java, multiple courses and interfaces, remote learning, and achieve a more user-friendly and usable system.…”

Section: Related Work: Computer-aided Assessmentmentioning

confidence: 99%

Customizable and scalable automated assessment of C/C++ programming assignments

Delgado‐Pérez

Medina‐Bulo

2020

Comp Applic In Engineering

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The correction of exercises in programming courses is a laborious task that has traditionally been performed in a manual way. This situation, in turn, delays the access by students to feedback that can contribute significantly to their training as future professionals. Over the years, several approaches have been proposed to automate the assessment of students' programs. Static analysis is a known technique that can partially simulate the process of manual code review performed by lecturers. As such, it is a plausible option to assess whether students' solutions meet the requirements imposed on the assignments. However, implementing a personalized analysis beyond the rules included in existing tools may be a complex task for the lecturer without a mechanism that guides the work. In this paper, we present a method to provide automated and specific feedback to immediately inform students about their mistakes in programming courses. To that end, we developed the CAC++ library, which enables constructing tailored static analysis programs for C/C++ practices. The library allows for great flexibility and personalization of verifications to adjust them to each particular task, overcoming the limitations of most of the existing assessment tools. Our approach to providing specific feedback has been evaluated for a period of three academic years in a course related to object-oriented programming. The library allowed lecturers to reduce the size of the static analysis programs developed for this course. During this period, the academic results improved and undergraduates positively valued the aid offered when undertaking the implementation of assignments.

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Section: Related Work: Computer-aided Assessmentmentioning

confidence: 99%

Customizable and scalable automated assessment of C/C++ programming assignments

Delgado‐Pérez

Medina‐Bulo

2020

Comp Applic In Engineering

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“…This quick turnaround opens up a system for students to iterate with multiple submissions. If grading is handled by a computer, then there is no human time cost to allowing students to receive repeated feedback over the course of an assignment [11]. This also frees instructors and teaching assistants to spend their time providing more hands-on and personal assistance for their students and decreases some barriers associated with large-course management.…”

Section: Programming Feedback Backgroundmentioning

confidence: 99%

Nudging student learning strategies using formative feedback in automatically graded assessments

Zamprogno

Holmes

Baniassad

2020

Proceedings of the 2020 ACM SIGPLAN Symposium on SPLASH-E

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Automated assessment tools are widely used as a means for providing formative feedback to undergraduate students in computer science courses while helping those courses simultaneously scale to meet student demand. While formative feedback is a laudable goal, we have observed many students trying to debug their solutions into existence using only the feedback given, while losing context of the learning goals intended by the course staff. In this paper, we detail two case studies from second and third-year undergraduate software engineering courses indicating that using only nudges about where students should focus their efforts can improve how they act on generated feedback. By carefully reasoning about errors uncovered by our automated assessment approaches, we have been able to create feedback for students that helps them to revisit the learning outcomes for the assignment or course. This approach has been applied to both multiplechoice quizzes and automated programming assessment. We have found that student performance has not suffered and that students reflect positively about how they investigate automated assessment failures.

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“…but not yet in clinical application, include Zr-(18.46-18.79)Nb-(0.47-1.1)Si (wt.%), [104] Zr-(19.5-20.5)Nb-(0.1-15)Ti (wt.%), [105] Zr-(16-50)Ta (wt.%), [106] and Ti-(10-20)Nb-(1-50)Zr (wt.%). [107] In addition, there are Zr-based BMGs, which are patented for biomedical application such as Zr50-x Al10Cu40Rex (at.%) (RE = Y or La or Ce or Nd, Sm, Gd or Dy or Ho or Er, Tm or Lu; X is 0 < X ≤10), [108] (60-70)Zr-25Ti-2.5Nb-7.5Al-15Fe-15Cu-10Ag-10Pd, [109] Zr48Cu45-x Al7Agx, (1 < X <10), [110] (ZraTib ), [111] [(ZrxCu100−x)a(EyG100−y)100−a]100−bZb, (70≤a≤90, x≥50, y>0, and 0≤b≤6). [112] A u t h o r M a n u s c r i p t…”

Section: A U T H O R M a N U S C R I P T A U T H O R M A N U S C R I P T A U T H O R M A N U S C R I P Tmentioning

confidence: 99%