A Framework For Hypothesis-Driven Approaches To Support Data-Driven Learning Analytics In Measuring Computational Thinking In Block-Based Programming

被引：8

作者：

Grover, Shuchi ^{[1
]}

Bienkowski, Marie ^{[1
]}

Basu, Satabdi ^{[1
]}

Eagle, Michael ^{[2
]}

Diana, Nicholas ^{[2
]}

Stamper, John ^{[2
]}

机构：

[1] SRI Int, 333 Ravenswood Ave, Menlo Pk, CA 94025 USA

[2] Carnegie Mellon Univ, HCII, Pittsburgh, PA 15213 USA

来源：

SEVENTH INTERNATIONAL LEARNING ANALYTICS & KNOWLEDGE CONFERENCE (LAK'17) | 2017年

关键词：

D O I：

10.1145/3027385.3029440

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

K-12 classrooms use block-based programming environments (BBPEs) for teaching computer science and computational thinking (CT). To support assessment of student learning in BBPEs, we propose a learning analytics framework that combines hypothesis- and data-driven approaches to discern students' programming strategies from BBPE log data. We use a principled approach to design assessment tasks to elicit evidence of specific CT skills. Piloting these tasks in high school classrooms enabled us to analyze student programs and video recordings of students as they built their programs. We discuss a priori patterns derived from this analysis to support data-driven analysis of log data in order to better assess understanding and use of CT in BBPEs.

引用

页码：530 / 531

页数：2

共 8 条

[1] [Anonymous], 2012, Journal of Educational Data Mining
[2] [Anonymous], 2012, P 43 ACM TECHN S COM, DOI [10.1145/2157136.2157182, DOI 10.1145/2157136.2157182]
[3] [Anonymous], 2006, Educational Measurement: Issues and Practice, DOI [10.1111/j.1745-3992.2006.00075.x, DOI 10.1111/J.1745-3992.2006.00075.X]
[4] Baker R, 2014, CAMBRIDGE HANDBOOK OF THE LEARNING SCIENCES, 2ND EDITION, P253
[5] Using Learning Analytics to Understand the Learning Pathways of Novice Programmers
Berland, Matthew
Martin, Taylor
Benton, Tom
Smith, Carmen Petrick
Davis, Don
[J]. JOURNAL OF THE LEARNING SCIENCES, 2013, 22 (04) : 564 - 599
[6] Robins A., 2003, COMPUT SCI EDUC, V13, P137, DOI [https://doi.org/10.1076/csed.13.2.137.14200, DOI 10.1076/CSED.13.2.137.14200, 10.1076/CSED.13.2.137.14200]
[7] Werner L, 2012, P 43 SIGCSE
[8] Winne P.H., 2013, J ED DATA MINING, V5, P1

← 1 →