Learning Strategies in Engineering Mathematics

Griese, Birgit

doi:10.1007/978-3-658-17619-8

Cited by 10 publications

(6 citation statements)

References 78 publications

(112 reference statements)

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“…The before mentioned findings concerning the high dropout rates in STEM degree programs suggest a missing congruence or fit between students and universities with respect to mathematics in STEM programs. Approaches for a better alignment between students and the academic environment at the universities include: (a) the facilitation of choices of study degree programs congruent to students' interests and abilities (see Lubinski and Benbow 2000), (b) mathematical transition courses that support mathematical abilities of new STEM students (e.g., Leviatan 2008;Griese 2017), and (c) better communication and coordination between schools and universities (recommended by Thomas et al 2015). All such approaches for better alignment require transparent information on both the person and environment side.…”

Section: Person-environment-(mis-)fitmentioning

confidence: 99%

Mathematical Prerequisites for STEM Programs: What do University Instructors Expect from New STEM Undergraduates?

Deeken

Neumann

Heinze

2019

Int. J. Res. Undergrad. Math. Ed.

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In many countries STEM degree programs face high dropout rates, which are often attributed to insufficient mathematical preparation of incoming undergraduates. However, it is largely unclear which mathematical prerequisites universities expect from their new STEM undergraduates. There are hardly official documents and it is even an open question whether there is a consensus among university instructors. The main goal of this study is therefore to describe the expected mathematical prerequisites for new STEM undergraduates. We conducted a Delphi study with German university instructors for first semester mathematics courses in STEM programs. The participants identified 179 prerequisites addressing: (1) mathematical content, (2) mathematical processes, (3) views about the nature of mathematics, and (4) personal characteristics. For 140 of these prerequisites there was a consensus regarding their necessity among the university instructors. The expected mathematical prerequisites are mainly on a basic level and do not require formalistic or abstract mathematical knowledge or abilities.

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Section: Person-environment-(mis-)fitmentioning

confidence: 99%

Mathematical Prerequisites for STEM Programs: What do University Instructors Expect from New STEM Undergraduates?

Deeken

Neumann

Heinze

2019

Int. J. Res. Undergrad. Math. Ed.

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“…Theoretically, elaboration strategies are expected to improve study performance because they lead to deep processing of the information, which should lead to deep and stable knowledge. Correlational data show connections of gaining an overview (i.e., using elaboration strategies) with performance in one study on mathematics (Eley & Meyer, 2004); however, this connection could not be confirmed in several other studies (Griese, 2017;Griese & Kallweit, 2017;Laging & Voßkamp, 2017;Liston & O'Donoghue, 2009; see also Schneider & Preckel, 2017 for results across different domains). Similarly, organization strategies do not correlate with students' performance (Griese, 2017).…”

Section: Learning Strategies and Performancementioning

confidence: 84%

“…Students' management of internal resources (effort) is an important predictor of academic performance both across different domains (Schneider & Preckel, 2017) and in university mathematics (Griese, 2017). In contrast, peer learning as a form of managing external resources has proven helpful in various domains (Schneider & Preckel, 2017) but not mathematics (Griese, 2017).…”

Section: Learning Strategies and Performancementioning

confidence: 99%

The role of learning strategies for performance in mathematics courses for engineers

Liebendörfer

Göller

Gildehaus

et al. 2022

International Journal of Mathematical Education in Science and

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We analyse the impact of learning strategies on engineering students' performance in mathematics. Learning strategies play an important role in self-regulated learning and are a possible predictor of student performance. Especially for mathematics-related learning strategies, the question arises how such strategies can be measured and how they relate to mathematics performance. Therefore, we present a new learning strategy questionnaire that takes into account the specifics of mathematical learning at universities. We then present correlational data of a longitudinal study with n = 403 engineering students. We further regress their performance on students' use of their learning strategies as well as their prior performance. The results indicate which learning strategies help students succeed.

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“…The Learning and Study Strategies Index (LASSI) by (Paul R. et al, 1991) and the Motivated Strategies for Learning Questionnaire (MSLQ) by (Weinstein et al, 2016) are two self-report instruments developed in earlier research to assess learning strategies. LASSI covers thoughts, behaviors, attitudes, and beliefs concerning successful learning experiences that interventions can cultivate (Griese, 2016). On the other hand, MSLQ was developed to determine the types of motivational factors and learning of college students (Kaldo & Õun, 2020).…”

Section: Phase 1: Identifying Motivation Factors and Learning Strategiesmentioning

confidence: 99%

A Conceptual Persuasive Development Framework to Change Students’ Behaviour in Massive Open Online Courses: A Review

Mhd Salim,

Mohamad Ali,

Jalaludin

et al. 2023

IJLTER

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Some experts attribute the relatively low completion rates of Massive Open Online Courses (MOOCs) partly to user dissatisfaction with the system. Live instructors are absent from MOOCs due to their delivery through virtual learning platforms. A distinctive feature distinguishing MOOCs from other e-learning systems is the significantly higher ratio between users and instructors. Consequently, the main challenges include limited interaction between students and study materials and the heightened need for instructor guidance. Consequently, enhancing the design of the existing MOOCs system is imperative to create a more engaging learning experience. Previous studies have attempted to incorporate persuasive design elements into e-learning systems. However, these studies must integrate persuasive design with motivational factors and effective learning strategies to encourage student behavior change and increase student engagement. The present study utilizes prior literature to establish a conceptual framework for persuasive e-learning development, known as PEDAL, which integrates motivational factors, learning strategies, and persuasive design principles. The initial section of the paper introduces issues related to student motivation, learning strategies, the MOOCs platform, and the potential impact of persuasive technology on enhancing the effectiveness of MOOCs. The subsequent section elucidates the methodology employed for the literature search. The third section explains the mechanisms of the PEDAL framework and discusses relevant previous literature that contributed to its development. Finally, the last section outlines the framework's limitations and potential future improvement. The paper also outlines how the proposed conceptual framework can be applied to design an effective e-learning system.

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Learning Strategies in Engineering Mathematics

Cited by 10 publications

References 78 publications

Mathematical Prerequisites for STEM Programs: What do University Instructors Expect from New STEM Undergraduates?

Mathematical Prerequisites for STEM Programs: What do University Instructors Expect from New STEM Undergraduates?

The role of learning strategies for performance in mathematics courses for engineers

A Conceptual Persuasive Development Framework to Change Students’ Behaviour in Massive Open Online Courses: A Review

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