The Construct Validity of Student Engagement: A Confirmatory Factor Analysis Approach

As institutions seek to promote student engagement on campus, the National Survey of Student Engagement (NSSE) is increasingly being used to chart progress and compare results using the Five Benchmark Scores. While recent research has begun to decompose the five benchmarks in a variety of ways; few research studies have sought to explore the underlying structure of these five benchmarks, their interdependence, and the extent to which the items do reflect those five dimensions. This study begins to address the instrument’s construct validity by submitting a single, first-time freshman cohort’s NSSE responses to a confirmatory factor analysis, and proposes as an alternative, eight “dimensions” of student engagement that fit this set of data slightly better and in a more useful way. Results have practical implications for institutions utilizing NSSE, but also contain conceptual implications pertaining to the application of these benchmarks.     

Integration of an XML electronic dictionary with linguistic tools for natural language processing

This study proposes the codification of lexical information in electronic dictionaries, in accordance with a generic and extendable XML scheme model, and its conjunction with linguistic tools for the processing of natural language. Our approach is different from other similar studies in that we propose XML coding of those items from a dictionary of meanings that are less related to the lexical units. Linguistic information, such as morphology, syllables, phonology, etc., will be included by means of specific linguistic tools. The use of XML as a container for the information allows the use of other XML tools for carrying out searches or for enabling presentation of the information in different resources. This model is particularly important as it combines two parallel paradigms—extendable labelling of documents and computational linguistics—and it is also applicable to other languages. We have included a comparison with the labelling proposal of printed dictionaries carried out by the Text Encoding Initiative (TEI). The proposed design has been validated with a dictionary of more than 145 000 accepted meanings.     

Connecting School and Home: Examining Parental and School Involvement in Readiness for College Through Multilevel SEM

Research in Higher Education - Parental involvement is widely acknowledged as a critical factor influencing the college choice process among families. What is not clear, though, is whether this...     

AI recruitment algorithms and the dehumanization problem

According to a recent survey by the HR Research Institute, as the presence of artificial intelligence (AI) becomes increasingly common in the workplace, HR professionals are worried that the use of recruitment algorithms will lead to a “dehumanization” of the hiring process. Our main goals in this paper are threefold: (i) to bring attention to this neglected issue, (ii) to clarify what exactly this concern about dehumanization might amount to, and (iii) to sketch an argument for why dehumanizing the hiring process is ethically suspect. After distinguishing the use of the term “dehumanization” in this context (i.e. removing the human presence) from its more common meaning in the interdisciplinary field of dehumanization studies (i.e. conceiving of other humans as subhuman), we argue that the use of hiring algorithms may negatively impact the employee-employer relationship. We argue that there are good independent reasons to accept a substantive employee-employer relationship, as well as an applicant-employer relationship, both of which are consistent with a stakeholder theory of corporate obligations. We further argue that dehumanizing the hiring process may negatively impact these relationships because of the difference between the values of human recruiters and the values embedded in recruitment algorithms. Drawing on Nguyen’s (in: Lackey, Applied Epistemology, Oxford University Press, 2021) critique of how Twitter “gamifies communication”, we argue that replacing human recruiters with algorithms imports artificial values into the hiring process. We close by briefly considering some ways to potentially mitigate the problems posed by recruitment algorithms, along with the possibility that some difficult trade-offs will need to be made.

     

“But I’m oppressed too”: white male college students framing racial emotions as facts and recreating racism

Most analyses of racism focus on what people think about issues of race and how this relates to racial stratification. This research applies Feagin’s white racial frame to analyze how White male college students at two universities feel about racism. Students at the academically non-selective and less diverse university tended to be apathetic while those attending the academically selective and more racially diverse campus tended to be angry. This study highlights the interconnectedness of affective and cognitive responses to race: two areas integral to both the maintenance and dismantling of systemic racism. It also highlights how men frequently frame emotions as facts, which can also support racial stratification.     

Making the Mark: Are Grades and Deep Learning Related?

Assessing gains in learning has received increased attention as one dimension of institutional accountability both in the USA (Arum and Roksa, Academically adrift: Limited learning on college campuses, 2011) and abroad (OECD, http://www.oecd.org/document/22/0,3746,en_2649_39263238_40624662_1_1_1_1,00.html, 2013, http://www.oecd.org/edu/skills-beyond-school/AHELOFSReportVolume2.pdf, 2012). Current approaches to assessing college learning are dominated by objective tests as well as student self-reported questionnaires, such as the National Survey of Student Engagement (NSSE). This study examined how the three NSSE deep approaches to learning scales contribute to the narrative on academic rigor at a large, public research institution. Using Confirmatory Factor Analyses and Structural Equation Modeling, results showed that the three deep approaches to learning constructs were internally valid, but deep learning was not related to GPA. Findings raised questions regarding good measurement of student learning and student reward for rigorous performance.     

A Tale of Two Groups: Differences Between Minority Students and Non-Minority Students in their Predispositions to and Engagement with Diverse Peers at a Predominantly White Institution

Using a revised version of the Transition to College Model (Locks et al. 2008, Rev High Educ 31(3), 257–285), this study examined the extent to which minority students and non-minority students differ in their predispositions to engage in campus-based diversity activities as well as in their engagement with ethnically diverse college peers at a predominantly White college. Findings indicate that engagement with diverse peers is a learned behavior; one that was shaped long before a student stepped into college. The importance of past interactions with diverse peers extends beyond freshman year predispositions to engage; students who interacted with diverse students prior to college were also more prone to report engagement with diverse peers at the end of their sophomore year. Notably, freshmen minority students were more predisposed to engage diverse peers next to their White peers; these ethnic-based differences, however, dissipated by the end of the sophomore year of college.     

The role of finances in the persistence process: A structural model

The study empirically examined the role of finances on college persistence by presenting a causal model that relied on several theoretical frameworks. A quantitative model was tested via linear structural equations for categorical data that incorporated constructs from the financial aid literature as well as the persistence literature. The research design was longitudinal in nature and analyses were conducted on a sample of 466 college students who were attending a large public urban commuter institution in the spring of 1989. In sum, results appear to suggest that financial aid, and its concomitant attitude, is important not only because it equalizes opportunities between affluent and low-income students, but also because it facilitates the integration of the student into the academic and social components of the institution as well as by influencing his or her commitment to stay in college.