Abstract
Work on university student absenteeism is an interesting topic that treats motivation problems and its important consequences, like dropout, but is not easy to measure. In this chapter, the authors make a revision of the concept and an empirical approach to the possible reasons of student absenteeism through multivariate analyses—which the students themselves believe to be justified—and those offered by the faculty members in the case of the authors’ big school (with nine studies and 12,000 students), of the authors’ university (with 70,000 students), in the authors’ country. The analysis was carried out on two samples (1,161 students and 181 professors), which indicates that the reasons offered by each population are not the same. Through a cluster analysis, it is possible to identify six student performance profiles, which sheds some light on understanding this fact and the opportunity to suggest some ways of action.
TopIntroduction
Paying attention to or carrying out controlled studies on the reasons and possible causes of university student absenteeism is uncommon. In fact, few are the contrasted data available on this phenomenon and, despite the fact that it is well known how common and usual it is, it is still peculiar that no attention has been paid to this matter in order to try to correct it or minimize it. Usually, the reasons given are very general vague explanations and, basically, more weight is given to the students’ detachment toward the lessons than to other more structural reasons which are never analyzed from an objective perspective. It seems, then, pertinent to carry out a small analysis of what we know about this phenomenon and which some of the papers dealing with this subject are (Barlow and Fleischer, 2011; Becker and Powers, 2001; Christie, Munro, and Fisher, 2004; Cohn and Johnson, 2006; Jaría, 2011; Moore, Armstrong, and Pearson, 2008; Romer, 1993; Sawon, Pembroke, and Wille, 2012; Sharmistha, 2012; Strauss and Volkwein, 2002; Tejedor and García-Valcárcel, 2007; Thomas, 2002; Van Den Berg and Hofman, 2005). They are not many; however, the scientific interest raised by this matter is illustrative.
The majority of papers that identify student absenteeism as a relevant variable do so as complementary data to academic performance (Becker & Powers, 2001; Chen & Lin, 2008; Cohn & Johnson, 2006; Durden & Ellis, 1995; Gracia & de la Iglesia, 2007; Marburger, 2001, 2006; Romer, 1993). The works by Fernández (2006) and McCarey, Barr, and Rattray (2007) correlate the estimated percentage of absenteeism with the grades obtained by the students and generate one construct only for academic performance where an attendance percentage estimate is included. This conception of absenteeism as a performance indicator is clearly realistic and very usual, although it takes no notice of those cases where absenteeism does not necessarily imply ignoring the courses and the grades. Obviously, the ways and mechanisms to obtain learning resources outside the university classroom are multiple and diverse.
Another stream of works is represented by those papers that refer to student absenteeism as a consequence of other factors exogenous to the university or the educational phenomenon. Bovet, Viswanathan, Faeh, and Warren (2006) establish that the first dependent variable to evaluate the undergraduates’ health is the absenteeism rate. Obviously, it seems too simplistic to attribute most of the absenteeism only to health reasons, at least, in the environment of our university system. Likewise, Donoso and Schiefelbein (2007) and Tatum (1992) discussed the effect of absenteeism due to social reasons. In fact, the latter author deals with the issue of racism in the classroom, which does not seem to be our case. In Christie et al. (2004) the comparison allows an analysis of the constraints and opportunities that all young people face during their time in higher education and the circumstances under which students decide to skip classes. Evidently, some isolated cases can occur, but their effect will not be significant in our university reality. In the same line of these works, we can include those studies that analyze university student absenteeism in minority populations, which are logically not the generators of the great number of absentees, but which must be considered1.
As can be seen above, it can be said that absenteeism has been little studied (or not at all) as a goal variable, and the proposals made have included it as a complement and always from the perspective of dropout or as an academic performance indicator along with a student profile with different characteristics and abilities.
Key Terms in this Chapter
University Dropout: Is not attending class and the non-fulfilment of the obligations in such a way as to affect the final efficiency of the whole of tasks of the university career.
Cluster Analysis: Cluster analysis divides data into groups (clusters) that are meaningful and useful. Meaningful groups are the goal, and then the clusters should capture the natural structure of the data.
Factorial Analysis: Statistical method used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved variables called factors.
Predictor Variables: Variables that can be used to predict the value of another variable, in our case as dropout or absenteeism of university students.
University Absenteeism: Persistent absence from class at university. This work it analyse causes for this phenomena, the possible reasons of student absenteeism through multivariate analyses.
Academic Achievement: Is used to describe actions related to the work done colleges and universities, in terms of achievement or results, Is focused especially in work which involves studying and reasoning, but also in practical or technical skills.