“I Wanted a Profession That Makes a Difference”—An Online Survey of First-Year Students’ Study Choice Motives and Sustainability-Related Attributes

Oberrauch, Anna; Mayr, Helga; Никитин, И. Н.; Bügler, Tanja; Kosler, Thorsten; Vollmer, Christian

doi:10.3390/su13158273

Cited by 17 publications

(23 citation statements)

References 103 publications

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“…Students' responses are then analyzed by a wide range of methodologies. SET's reliability as a teaching and learning quality indicator has been debated among pedagogical researchers [27,28], especially regarding whether an ordinal scale can capture quality in teaching and learning, which is considered to be a multidimensional attribute [29][30][31].…”

Section: Student Evaluation Of Teachingmentioning

confidence: 99%

Leveraging AI and Machine Learning for National Student Survey: Actionable Insights from Textual Feedback to Enhance Quality of Teaching and Learning in UK’s Higher Education

et al. 2022

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Students’ evaluation of teaching, for instance, through feedback surveys, constitutes an integral mechanism for quality assurance and enhancement of teaching and learning in higher education. These surveys usually comprise both the Likert scale and free-text responses. Since the discrete Likert scale responses are easy to analyze, they feature more prominently in survey analyses. However, the free-text responses often contain richer, detailed, and nuanced information with actionable insights. Mining these insights is more challenging, as it requires a higher degree of processing by human experts, making the process time-consuming and resource intensive. Consequently, the free-text analyses are often restricted in scale, scope, and impact. To address these issues, we propose a novel automated analysis framework for extracting actionable information from free-text responses to open-ended questions in student feedback questionnaires. By leveraging state-of-the-art supervised machine learning techniques and unsupervised clustering methods, we implemented our framework as a case study to analyze a large-scale dataset of 4400 open-ended responses to the National Student Survey (NSS) at a UK university. These analyses then led to the identification, design, implementation, and evaluation of a series of teaching and learning interventions over a two-year period. The highly encouraging results demonstrate our approach’s validity and broad (national and international) application potential—covering tertiary education, commercial training, and apprenticeship programs, etc., where textual feedback is collected to enhance the quality of teaching and learning.

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Section: Student Evaluation Of Teachingmentioning

confidence: 99%

Leveraging AI and Machine Learning for National Student Survey: Actionable Insights from Textual Feedback to Enhance Quality of Teaching and Learning in UK’s Higher Education

et al. 2022

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“…Therefore, counsellors in the university system need to reach out to these students, enabling them to participate in programmes offered by the university, directing them to align their chosen fields of study with their knowledge, skills and abilities and the needs of the labour market and the broader South African economy. Therefore, first-year students can be supported in the complex process of planning an appropriate career path and become more proactive in career decisionmaking ( Jemini-Gashi et al, 2021;Oberrauch et al, 2021).…”

Section: Practical Implicationsmentioning

confidence: 99%

Psychometric Properties of a Short Measure for South African First-Year Students’ Perceptions of Fit with their Courses of Study

Machaba¹,

Mostert²

2022

JSAA

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The first year of higher education is one of the most critical and challenging times in a student’s lifeand choosing a specific course of study can be very difficult. Often, first-year students realize theyhave different expectations from the courses of study they chose and perceive that their abilities,skills, interests, and ambitions for a future career do not match their chosen courses of study. Whenthe wrong choice has been made, and there is no intervention to choose a course with a moreappropriate fit, students may decide to leave university prematurely, which may have a major impacton a student’s life. Identifying students who do not experience alignment with their choice of studycourse, and offering these students assistance and guidance, is imperative for universities to retainas many students as possible. Unfortunately, there is a paucity of research on a short scale thatmeasures student-course fit validly and reliably, and which can be fairly applied to different groupsin an unbiased manner. This study analyses the psychometric properties of a measure of students’perceptions of fit with their course of study, adapted from a widely used person-job fit scale. Statisticaltechniques used to determine the validity and reliability of this scale were structural validity,differential item functioning to determine item bias, measurement invariance, and reliability.A quantitative, cross-sectional design was used. A sample (N = 1,211) of South African first-yearuniversity students studying at a university with three different campuses was used. As expected,confirmatory factor analysis provided evidence of a one-factor structure. No item bias was presentfor language and gender groups. Although item bias was present for item 2 between campuses,the post hoc analysis indicated that the impact was practically negligible. Measurement invariancewas established, as well as good reliability of the scale. The findings of this study can contribute toknowledge concerning the valid, reliable and fair measurement of first-year students’ perceived fitwith their courses of study. In addition, insights could assist universities in identifying students whoneed proper career guidance to better match with their chosen courses of study.

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“…Therefore, HEIs play an important role in advancing SD [21,22], as well as pursuing agendas to educate students as global citizens [23]. Students must be prepared to act as persuasive citizens and "agents of change" in their future professional environment and participate in the changes necessary for social transformation toward SD [3,24]. The aim is not to educate students to behave in a certain way but rather to enable them to think critically about SD-related issues and to get them to participate in SD training [20].…”

Section: Introductionmentioning

confidence: 99%

The Students’ Intrinsic Motivation for Learning Non-Financial Information Matters from Their Self-Identification as Global Citizens

2023

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Recent developments related to non-financial information (NFI) reporting encourage the adoption of a long-term vision approach to sustainable development, which is also behind the definition of global citizens. In turn, the self-determination theory (SDT) describes which elements explain the students’ motivation. Using NFI as the subject and the SDT as the theoretical framework, this paper aims to identify the elements that can explain accounting students’ intrinsic motivation to learn topics related to NFI. Those elements include the different aspects that integrate the concept of a global citizen as well as sociodemographic variables. Data were gathered from a questionnaire to accounting students in Portugal. Through factorial analysis and linear regression, the findings indicate that all the elements underlying the concept of a global citizen, except empathy, are relevant in explaining students’ intrinsic motivation. Conversely, sociodemographic variables were not relevant for this purpose, which may indicate a more integrated perspective of the learning usefulness of topics related to NFI by self-identified students as global citizens. This paper provides insights into how students of an accounting course are intrinsically motivated to acquire skills in NFI reporting, which is particularly relevant to higher education institutions (HEIs), professors, students, and organizations related to accountancy education.

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“I Wanted a Profession That Makes a Difference”—An Online Survey of First-Year Students’ Study Choice Motives and Sustainability-Related Attributes

Cited by 17 publications

References 103 publications

Leveraging AI and Machine Learning for National Student Survey: Actionable Insights from Textual Feedback to Enhance Quality of Teaching and Learning in UK’s Higher Education

Leveraging AI and Machine Learning for National Student Survey: Actionable Insights from Textual Feedback to Enhance Quality of Teaching and Learning in UK’s Higher Education

Psychometric Properties of a Short Measure for South African First-Year Students’ Perceptions of Fit with their Courses of Study

The Students’ Intrinsic Motivation for Learning Non-Financial Information Matters from Their Self-Identification as Global Citizens

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