Does computer-aided formative assessment improve learning outcomes?

Hannah, John; James, Alex; Williams, Phillipa

doi:10.1080/0020739x.2013.822583

Cited by 9 publications

(5 citation statements)

References 8 publications

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“…These results are promising, and coincide with the findings of other research regarding the usefulness of automated FA with regard to student performance and perception [3], [10], [19], [27], [35]. In particular, we agree with Gikandi, Morrow & Davis who, after a meta-analysis in which the review 18 empirical studies, state that such approach can foster a learner focus and enhance student engagement with valuable learning experiences [38].…”

Section: Discussionsupporting

confidence: 90%

A Framework for Automated Formative Assessment in Mathematics Courses

Moreno

Pineda

2020

IEEE Access

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In general terms, the aims of formative assessment are to gather and analyze information about the progress of students, with the intention of improving instruction in real time. This can also help teachers to identify those elements that students are struggling with, and therefore to determine where adjustments must be made. There are, however, some issues that must be addressed before this approach can become more widespread. In this research, and with mathematics as our specific scope, we focus on two of these issues: we present a framework to help teachers implement assessment items that are highly dynamic and flexible, but that allows them at the same time to model common misconceptions and to provide suitable feedback. Following this, we carried out an experiment to determine (i) whether the students perceived such feedback to be useful; (ii) whether they actually used it; and ultimately (iii) whether it produced a difference in their performance. To find the answers to these questions, we used an online 'pre-calculus' course with 458 students and collected data directly from those students via both a perception survey and from their interaction with the LMS. Based on these data, we performed several analyses, including process mining and statistical hypothesis tests. The results demonstrated that although many of the students had a positive opinion of the feedback received, it was not always followed. Nevertheless, when it was (or at least when students said it was), the students performed better.

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Section: Discussionsupporting

confidence: 90%

A Framework for Automated Formative Assessment in Mathematics Courses

Moreno

Pineda

2020

IEEE Access

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“…In terms of improving student learning, Hannah, James and Williams (2014) found that, in their weekly formative online assessments which allowed multiple attempts, some students spent too long perfecting these exercises at the expense of studying higher-level examination-type questions, which Lingard et al (2009, 604) observed are difficult to set in this format. This danger was flagged by Ramsden (1992, 189), when he stated that the "separate assessment of basic skills and knowledge, unless clearly flagged as a relatively unimportant part of the whole assessment process, leads to a focus by students on these activities rather than on more complex ones that are related to understanding."…”

Section: Practical Notes For Implementationmentioning

confidence: 99%

Formative assessment in mathematics for engineering students

Fhloinn

Carr²

2017

European Journal of Engineering Education

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In this paper, we present a range of formative assessment types for engineering mathematics, including in-class exercises, homework, mock examination questions, table quizzes, presentations, critical analyses of statistical papers, peerto-peer teaching, online assessments and electronic voting systems. We provide practical tips for the implementation of such assessments, with a particular focus on time or resource constraints and large class sizes, as well as effective methods of feedback. In addition, we consider the benefits of such formative assessments for students and staff.

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“…Hannah, James, and Williams [8] found that among first year mathematics students, online results in formative assessment tasks correlated well with final examination marks, but this could be mainly a reflection of existing differences in ability and previous studies completed.…”

Section: Comparing Outcomesmentioning

confidence: 96%

Effects of a change to more formative assessment among tertiary mathematics students

Freislich

Bowen-James²

2020

ANZIAMJ

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A change in teaching delivery at a large Australian university, from two semesters to three trimesters, was the occasion for using more formative assessment in a core first-year mathematics unit. This study compared evidence about learning outcomes for two cohorts in adjacent years. Cohort 1 was the last taught over a semester, and Cohort 2 the first taught over a trimester. There was no change in overall workload, and no change in the unit's total teaching hours, syllabus or materials. Assessments were changed for class tests during the teaching period by giving Cohort 2 access to unlimited practice and computer-assisted feedback on the questions in the test database, followed by doing the tests under examination conditions. For Cohort 2, a written assignment was also added, focused on giving a clear solution to a mathematics problem, and awareness of the need for appropriate evidence, both background and internal to the problem. Learning outcomes were compared using closely comparable tasks from the final examinations, and examining students' answers in the examination scripts. Outcomes were assessed by a method derived from the solo taxonomy, which afforded a common scale to measure the quality of learning outcomes observable in final examination scripts. Results on separate tasks, plus those for a composite score, favoured Cohort 2. The effect size for the composite score was 0.457. This indicates that the unlimited practice with computer feedback for class tests, and the writing assignment, were functioning as intended in promoting learning with understanding. References S. Bengmark, H. Thunberg, and T. M. Winberg. Success-factors in transition to university mathematics. Int. J. Math. Ed. Sci. Tech., 48(7):988–1001, 2017. doi:10.1080/0020739X.2017.1310311. J. B. Biggs and K. F. Collis. Evaluating the quality of learning: The SOLO taxonomy. Academic Press, New York, 1981. URL https://www.elsevier.com/books/evaluating-the-quality-of-learning/biggs/978-0-12-097552-5. A. Bowen-James. Perceptions of learning environments among tertiary mathematics students. Sc.Ed.D. Thesis. Curtin University of Technology, 2002. H. Chick, J. M. Watson, and K. F. Collis. Using the solo taxonomy for error analysis in mathematics. Res. Math. Ed. Aust., 1(1):34–47, 1988. M. R. Freislich. A comparison between the effects of Keller Plan instruction and traditional teaching methods on the structure of learning outcomes among tertiary mathematics students. Sc.Ed.D. Thesis. Curtin University of Technology, 1997. M. R. Freislich. The effects of Keller Plan instruction on the achievement and attitudes of tertiary mathematics students. Proc. Int. Conf. Teach. Math., Istanbul. 2006. M. Gill and M. Greenow. How effective is feedback in computer-aided assessment? Learn. Media Tech., 33(3):207–220, 2008. doi:10.1080/17439880802324145. J. Hannah, A. James, and P. Williams. 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Online Learn. Teach., 5(4):618–629, 2009. URL https://jolt.merlot.org/vol5no4/hodge_1209.htm. D. Holton and D. Clarke. Scaffolding and metacognition. Int. J. Math. Ed. Sci. Tech., 37(2):127–143, 2006. doi:10.1080/00207390500285818. A. H. Jonsdottir, A. Bjornsdottir, and G. Stefansson. Difference in learning among students doing pen-and-paper homework compared to web-based homework in an introductory statistics course. J. Stat. Ed., 25(1):12–20, 2017. doi:10.1080/10691898.2017.1291289. M. McAlinden and A. Noyes. Mathematics in the disciplines at the transition to university. Teach. Math. App., 38(2):61–73, 2019. doi:10.1093/teamat/hry004. J. Nicholas, L. Poladian, J. Mack, and R. Wilson. Mathematics preparation for university: entry pathways and their effect on performance in first year mathematics and science subjects. Int. J. Innov. Sci. Math. Ed., 23(1):37–51, 2015. https://openjournals.library.sydney.edu.au/index.php/CAL/article/view/8488. M. I. Nunez-Pena, R. Bono, and M. Suarez-Pellicioni. Feedback on students' performance: a possible way of reducing the negative effect of math anxiety in higher education. Int. J. Ed. Res., 70(1):80–87, 2015. doi:10.1016/j.ijer.2015.02.005. J. T. E. Richardson. Student learning in higher education: a commentary. Ed. Psych. Rev., 29(1):353–362, 2017. doi:10.1007/s10648-017-9410-x. L. J. Rylands and D. Shearman. Mathematics learning support and engagement in first year engineering. Int. J. Math. Ed. Sci. Tech., 49(8):1133–1147, 2018. doi:10.1080/0020739X.2018.1447699. K. A. Seaton. Efficacy and efficiency in formative assessment: an informed reflection on the value of partial marking. Int. J. Math. Ed. Sci. Tech., 44(7):963–971, 2013. doi:10.1080/0020739X.2013.831490. D. Wood, J. S. Bruner, and G. Ross. The role of tutoring in problem solving. J. Child Psychol. Psych., 17(1):89–100, 1976. doi:10.1111/j.1469-7610.1976.tb00381.x. L. Zetterqvist. Applied problems and use of technology in an aligned way in basic courses in probability and statistics for engineering students—a way to enhance understanding and increase motivation. Teach. Math. App., 36(2):108–122, 2017. doi:10.1093/teamat/hrx004.

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Does computer-aided formative assessment improve learning outcomes?

Cited by 9 publications

References 8 publications

A Framework for Automated Formative Assessment in Mathematics Courses

A Framework for Automated Formative Assessment in Mathematics Courses

Formative assessment in mathematics for engineering students

Effects of a change to more formative assessment among tertiary mathematics students

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