Mathematics learning support and engagement in first year engineering

Rylands, Leanne; Shearman, Donald

doi:10.1080/0020739x.2018.1447699

Cited by 24 publications

(20 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Support that helps students to start the learning process by engaging in mathematical tasks should therefore be beneficial. The results of Rylands and Shearman [19] are compatible with such a claim. They found, in an Australian context, that engagement in mathematical activity offered in a learning support program was positively related to achievement.…”

Section: Student Learningsupporting

confidence: 56%

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

Freislich

Bowen-James²

2020

ANZIAMJ

View full text Add to dashboard Cite

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. Does computer-aided formative assessment improve learning outcomes? Int. J. Math. Ed. Sci. Tech., 45(2):269–281, 2014. doi:10.1080/0020739X.2013.822583. D. Harris and M. Pampaka. \T1\textquoteleft they [the lecturers] have to get through a certain amount in an hour\T1\textquoteright : first year students\T1\textquoteright problems with service mathematics lectures. Teach. Math. App., 35(3):144–158, 2016. doi:10.1093/teamat/hrw013. S. Higgins and M. Katsipataki. Communicating comparative findings from meta-analysis in educational research: some examples and suggestions. Int. J. Math.. Res. Meth. Ed., 39(3):237–254, 2016. doi:10.1080/1743727X.2016.1166486. P. W. Hillock and R. N. Khan. A support learning programme for first-year mathematics. Int. J. Math. Ed. Sci. Tech., 50(7):24–29, 2019. doi:10.1080/0020739X.2019.1656830. A. Hodge, J. C. Richardson, and C. S. York. The impact of a web-based homework tool in university algebra courses on student learning and strategies. J. 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.

show abstract

Section: Student Learningsupporting

confidence: 56%

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

Freislich

Bowen-James²

2020

ANZIAMJ

View full text Add to dashboard Cite

show abstract

“…According to Rylands and Shearman (2018), our engagement level of 48% of the cohort is above what they had reviewed in the literature for other mathematics support services (all were below 35%), though smaller than their own rate (58%). Like us, they attributed their high rate to the fact that the support tutorials they offered were timetabled and that three of them were compulsory.…”

Section: Discussionmentioning

confidence: 57%

“…This often makes mathematics support a positive contribution to student progression and retention (Berry et al, 2015) and academic performance (Mac an Bhaird et al, 2009;Jacob and Ní Fhloinn, 2018). Students who engage with mathematics support tend to do better than those who do not, especially in subjects which include mathematics summative assessments, such as engineering (Lee et al, 2008;Rylands and Shearman, 2018).…”

Section: Introductionmentioning

confidence: 99%

Using Incoming Data to Monitor Engagement and Inform Mathematics Support Interventions

Lignos

2019

MSOR

View full text Add to dashboard Cite

Students who do not engage enough with their studies could place themselves at risk of underperforming or failing. Such a risk may be higher for students who are assessed in one or more mathematics modules and lack the appropriate background knowledge, or do not engage enough with related teaching activities. It has been shown for students who engage with mathematics support, there is a significant impact on student performance and progression in the relevant modules. Thus, improving the mechanisms of engagement with mathematics support should be a priority for any student success strategy.We discuss the monitoring of attendance and performance data of first-year engineering students, as it becomes available, in order to inform interventions which suit the observed student behaviour best. Specifically, the method described was used with first-year engineering students at the University of East London (UEL) during the 2017-8 academic year. We find that when monitoring processes are applied to an already tailored support package, they can often help maintain engagement levels, understand why some students do not engage, and prompt us to differentiate support further.

show abstract

“…The difficulties of evaluating mathematics support are well known [95]. Studies continue to note the significant challenges of establishing causal relationships [97] and in demonstrating statistically significant findings [98,99]. Indeed, whilst in one study a regression analysis demonstrated that for at-risk students, their qualifications prior to entry, diagnostic tests scores, and either their number of visits or time spent within mathematics support were predictors of their final mathematics grade, these variables did not explain the level of the variance observed within their overall grades [100].…”

Section: The Development Of Evaluation In Mathematics Supportmentioning

confidence: 99%

The evolution of mathematics support: a literature review

Lawson

Grove

Croft

2019

International Journal of Mathematical Education in Science and

View full text Add to dashboard Cite

Mathematics support, the provision of additional learning opportunities to, primarily, nonmathematics specialist undergraduates has grown significantly since the early 1990s, particularly in the UK, Ireland and Australia. Alongside the growth in volume of provision, there has been a marked increase in the amount of research and scholarship relating to mathematics support that has been carried out and disseminated. This paper reviews this literature and in doing so identifies areas in which mathematics support has evolved. This evolution has taken place in response to a range of crucial changes in the external policy and general environment and, in particular, in response to the changing nature of the so-called 'Mathematics Problem'. Key themes that emerge from the literature review, which are explored in detail, are the characteristics of students who engage with mathematics support and reasons why others do not; the role of the mathematics support tutor, who undertakes the tutoring task and how they are trained; the positioning of mathematics support within higher education structures; and the evaluation of the effectiveness of mathematics support.

show abstract

Mathematics learning support and engagement in first year engineering

Cited by 24 publications

References 12 publications

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

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

Using Incoming Data to Monitor Engagement and Inform Mathematics Support Interventions

The evolution of mathematics support: a literature review

Contact Info

Product

Resources

About