Employing self-assessment, journaling, and peer sharing to enhance learning from an online course

This study explored the use of self-assessments, journaling, and peer sharing in an online computer programming course. We conducted an experiment using a pretest–intervention–posttest design in which 64 undergraduate first-year students participated. We aimed to investigate whether self-assessment, journaling, and peer sharing can facilitate students’ learning. Moreover, we examined how the research variables related to each other and to learning achievement. Therefore, after the experiment, (1) prior knowledge, learning performance, and achievement were assessed, (2) online logs representing learning behaviors were analyzed, and (3) students were interviewed. Results demonstrated that self-assessment, journaling, and peer sharing effectively facilitated learning and students’ cognition regulation strategies. Namely, keeping a learning journal enabled students to summarize key concepts, elaborate ideas, and reflect on learning material; self-assessment allowed students to reflect on their understanding of the material under study; and peer sharing enabled students to study peers’ learning journals and self-assessments to improve their own. Although self-assessment, journaling, and peer sharing significantly correlated with each other and with learning achievement, results showed that keeping a learning journal had the strongest effect on learning achievement. Moreover, self-assessment and keeping a learning journal complemented each other and combining the two resulted in even higher learning achievement scores. The findings suggest that the use of self-assessment, journaling, and peer sharing show promise to facilitate learning from an online course.

[1]  L. Nilson Teaching at Its Best: A Research-Based Resource for College Instructors , 1997 .

[2]  Chwee Beng Lee,et al.  Examining Intentional Knowing Among Secondary School Students: Through the Lens of Metacognition , 2013 .

[3]  Jacob Cohen,et al.  Applied multiple regression/correlation analysis for the behavioral sciences , 1979 .

[4]  G. Hutcheson The Multivariate Social Scientist: Introductory Statistics Using Generalized Linear Models , 1999 .

[5]  Wu-Yuin Hwang,et al.  A study of a multimedia web annotation system and its effect on the EFL writing and speaking performance of junior high school students , 2011, ReCALL.

[6]  Yueh-Min Huang,et al.  How do we inspire people to contact aboriginal culture with Web2.0 technology? , 2015, Comput. Educ..

[7]  A. Renkl,et al.  Do learning protocols support learning strategies and outcomes? The role of cognitive and metacognitive prompts , 2007 .

[8]  Zehra Akyol,et al.  Assessing metacognition in an online community of inquiry , 2011, Internet High. Educ..

[9]  David L. Hicks,et al.  Supporting personalization and customization in a collaborative setting , 2003, Comput. Ind..

[10]  Gwo-Jen Hwang,et al.  A web-based programming learning environment to support cognitive development , 2008, Interact. Comput..

[11]  Dpto,et al.  Web Assisted Self-assessment in Computer Programming Learning Using AulaWeb * , .

[12]  Rustam Shadiev,et al.  A pilot study: Facilitating cross-cultural understanding with project-based collaborative learning in an online environment , 2015 .

[13]  Chaoyun Liang,et al.  Is Reflection Performance Correlated to the Learning Effect in a Web-Based Portfolio Assessment Environment for Middle School Students? , 2014 .

[14]  Wu-Yuin Hwang,et al.  A pilot study of cooperative programming learning behavior and its relationship with students' learning performance , 2012, Comput. Educ..

[15]  Thad Crews,et al.  Using technology to bring abstract concepts into focus: A programming case study , 2002, J. Comput. High. Educ..

[16]  Anthony R. Artino,et al.  Think, feel, act: motivational and emotional influences on military students’ online academic success , 2009, J. Comput. High. Educ..

[17]  Shih-Ching Yeh,et al.  Effects of Unidirectional vs. Reciprocal Teaching Strategies on Web-Based Computer Programming Learning , 2014 .

[18]  Paul Roe,et al.  A Web Based Environment for Learning to Program , 2003, ACSC.

[19]  Manoochehr Jafarigohar,et al.  The Effects of Hypertext Gloss on Comprehension and Vocabulary Retention under Incidental and Intentional Learning Conditions. , 2012 .

[20]  B. Wong,et al.  Effects of Guided Journal Writing on Students' Story Understanding , 2002 .

[21]  Susan Matlock-Hetzel Basic Concepts in Item and Test Analysis. , 1997 .

[22]  William E. Martin,et al.  Quantitative and Statistical Research Methods: From Hypothesis to Results , 2012 .

[23]  G. Hutcheson The Multivariate Social Scientist , 1999 .

[24]  Benjamin S. Bloom,et al.  A Taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom's Taxonomy of Educational Objectives , 2000 .

[25]  Stephen M. Alessi,et al.  Multimedia for Learning: Methods and Development , 2000 .

[26]  Nian-Shing Chen,et al.  Effects of reviewing annotations and homework solutions on math learning achievement , 2011, Br. J. Educ. Technol..

[27]  Gregory Schraw,et al.  Assessing metacognitive awareness , 1994 .

[28]  B. Bloom,et al.  Taxonomy of Educational Objectives. Handbook I: Cognitive Domain , 1966 .

[29]  Bruce C. Howard,et al.  Measures of children's knowledge and regulation of cognition , 2002 .

[30]  Nian-Shing Chen,et al.  Review of Speech-to-Text Recognition Technology for Enhancing Learning , 2014, J. Educ. Technol. Soc..

[31]  Shu-Hsien Huang,et al.  Embedding diagnostic mechanisms in a digital game for learning mathematics , 2013, Educational Technology Research and Development.