Developing student’s accounting competencies using Astin’s I-E-O model: an identification of key educational inputs based on Indonesian student perspectives

This paper discusses a model for developing Students’ Accounting Competencies (SAC) using Astin’s Input-Environment-Outcome (I-E-O) model. SAC based on AICPA core competency is considered important due to business and environment changes. Student Motivation, Student Previous Achievement, Student Demographic Characteristics, Learning Facilities, and Comfort of Class Size are educational inputs. Student Engagement and SAC are proxies for Environment and Outcome respectively. Empirically, the aforementioned educational inputs except Student Demographic Characteristics are important inputs for improving SAC. Student Engagement effectively mediates the influence of inputs on SAC. The I-E-O model is appropriate for analysing relationships among a single input, Student Engagement, and SAC. This model becomes less powerful for analysing simultaneous relationships among multiple inputs, Student Engagement, and SAC. Future research on using other assessments for gauging SAC, identifying other significant inputs, identifying the impact of real class size on Student Engagement and SAC, and developing Student Engagement for accounting courses are required

Academic Performance and Behavioral Patterns

Identifying the factors that influence academic performance is an essential part of educational research. Previous studies have documented the importance of personality traits, class attendance, and social network structure. Because most of these analyses were based on a single behavioral aspect and/or small sample sizes, there is currently no quantification of the interplay of these factors. Here, we study the academic performance among a cohort of 538 undergraduate students forming a single, densely connected social network. Our work is based on data collected using smartphones, which the students used as their primary phones for two years. The availability of multi-channel data from a single population allows us to directly compare the explanatory power of individual and social characteristics. We find that the most informative indicators of performance are based on social ties and that network indicators result in better model performance than individual characteristics (including both personality and class attendance). We confirm earlier findings that class attendance is the most important predictor among individual characteristics. Finally, our results suggest the presence of strong homophily and/or peer effects among university students

Class attendance, peer similarity, and academic performance in a large field study

Identifying the factors that determine academic performance is an essential part of educational research. Existing research indicates that class attendance is a useful predictor of subsequent course achievements. The majority of the literature is, however, based on surveys and self-reports, methods which have well-known systematic biases that lead to limitations on conclusions and generalizability as well as being costly to implement. Here we propose a novel method for measuring class attendance that overcomes these limitations by using location and bluetooth data collected from smartphone sensors. Based on measured attendance data of nearly 1,000 undergraduate students, we demonstrate that early and consistent class attendance strongly correlates with academic performance. In addition, our novel dataset allows us to determine that attendance among social peers was substantially correlated ($>$0.5), suggesting either an important peer effect or homophily with respect to attendance

The academic value of internships: benefits across disciplines and student backgrounds

While student benefits from internship experience have been frequently documented in research, the emphasis has been on internship effects on employment and career indicators. This work is concerned with effects on academic outcomes and focuses on the robustness of such effects across academic disciplines as well as for different achievement levels of students, student gender, and ethnicity. We present findings from a longitudinal sample (n > 15,000) that covers an extensive range of subjects and disciplines for large Undergraduate cohorts. Main effects and interactions for student background characteristics were investigated showing stable academic benefits for advantaged and disadvantaged students. Further, using ordinal logistic multi-level modelling, we explored the impact on the probability of attaining a higher degree classification for different student scenarios, thus illustrating the practical significance of these internship effects. Effects are less likely to stem from maturation or self-selection. Findings are therefore discussed against a background of motivational approaches suitable to integrate both direct and indirect paths from internship experience to academic outcomes to career indicators

Predicting success in graduate entry medical students undertaking a graduate entry medical program (GEM)

Background: Success in undergraduate medical courses in the UK can be predicted by school exit examination (A level) grades. There are no documented predictors of success in UK graduate entry medicine (GEM) courses. This study looks at the examination performance of GEM students to identify factors which may predict success; of particular interest was A level score. Methods: Data was collected for students graduating in 2004, 2005 and 2006, including demographic details (age and gender), details of previous academic achievement (A level total score and prior degree) and examination results at several points during the degree course. Results: Study group comprised 285 students. Statistical analyses identified no significant variables when looking at clinical examinations. Analysis of pass/fail data for written examinations showed no relationship with A level score. However, both percentage data for the final written examination and the analysis of the award of honours showed A level scores of AAB or higher were associated with better performance (p < 0.001). Discussion: A prime objective of introducing GEM programs was to diversify admissions to medical school. In trying to achieve this, medical schools have changed selection criteria. The findings in this study justify this by proving that A level score was not associated with success in either clinical examinations or passing written examinations. Despite this, very high achievements at A level do predict high achievement during medical school. Conclusions: This study shows that selecting graduate medical students with the basic requirement of an upper-second class honours degree is justifiable and does not disadvantage students who may not have achieved high scores in school leaver examinations

Predictors of Adolescents’ Interest in Stem Majors and Careers

Advanced Research Winner 2019: The United States currently faces a shortage of qualified workers in fields related to science, technology, engineering, and math (STEM). The first critical step in preventing the labor shortage in STEM is understanding the factors that guide adolescents toward STEM pursuits. Drawing on Eccles’ expectancy-value theory (EVT), the current study aims to identify factors that are relevant to adolescents’ interest in STEM majors and careers. Data were collected from 629 adolescents (Mage = 16.09). Participants attended a high school in northern California and predominantly identified as Asian American (82% of the sample). Preliminary analyses revealed that adolescent boys had higher STEM self-expectancies than did adolescent girls, whereas there was no gender difference in STEM values. Consistent with expectations, multiple regression demonstrated that STEM self-expectancies and values accounted for a significant amount of variance in participants’ interest in STEM majors and careers. STEM value was an especially strong predictor; adolescents tended to be most interested in STEM pursuits when they were also high in STEM value. Moderation analyses showed that the association between STEM value and interest in STEM majors and careers was stronger for girls than for boys. As a whole, this study’s findings suggest that valuing and enjoying STEM pursuits during high school could be an important antecedent of pursuing a STEM major and a STEM career later in life

Predicting academic achievement: The role of Motivation and Learning Strategies

The aim of this study consists in testing a predictive model of academic achievement including motivation and learning strategies as predictors. Motivation is defined as the energy and the direction of behaviors; it is categorized in three types of motivation –intrinsic, extrinsic and amotivation (Deci & Ryan, 1985). Learning strategies are deliberate operations oriented towards information processing in academic activities (Valle, Barca, González & Núñez, 1999). Several studies analysed the relationship between motivation and learning strategies in high school and college environments. Students with higher academic achievement were intrinsically motivated and used a wider variety of learning strategies more frequently. A non-experimental predictive design was developed. The sample was composed by 459 students (55.2% high-schoolers; 44.8% college students). Data were gathered by means of sociodemographic and academic surveys, and also by the local versions of the Academic Motivation Scale –EMA, Echelle de Motivation en Éducation (Stover, de la Iglesia, Rial Boubeta & Fernández Liporace, 2012; Vallerand, Blais, Briere & Pelletier, 1989) and the Learning and Study Strategies Inventory –LASSI (Stover, Uriel & Fernández Liporace, 2012; Weinstein, Schulte & Palmer, 1987). Several path analyses were carried out to test a hypothetical model to predict academic achievement (Kline, 1998). Results indicated that self-determined motivation explained academic achievement through the use of learning strategies. The final model obtained an excellent fit (χ2=16.523, df= 6, p=0.011; GFI=0.987; AGFI=0.955; SRMR=0.0320; NFI=0.913; IFI=0.943; CFI=0.940). Results are discussed considering Self Determination Theory and previous research.Fil: Stover, Juliana Beatriz. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires; ArgentinaFil: Freiberg Hoffmann, Agustín. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires; ArgentinaFil: de la Iglesia, Guadalupe. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Fernandez Liporace, Maria Mercedes. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

The academic backbone: longitudinal continuities in educational achievement from secondary school and medical school to MRCP(UK) and the specialist register in UK medical students and doctors

Background: Selection of medical students in the UK is still largely based on prior academic achievement, although doubts have been expressed as to whether performance in earlier life is predictive of outcomes later in medical school or post-graduate education. This study analyses data from five longitudinal studies of UK medical students and doctors from the early 1970s until the early 2000s. Two of the studies used the AH5, a group test of general intelligence (that is, intellectual aptitude). Sex and ethnic differences were also analyzed in light of the changing demographics of medical students over the past decades. Methods: Data from five cohort studies were available: the Westminster Study (began clinical studies from 1975 to 1982), the 1980, 1985, and 1990 cohort studies (entered medical school in 1981, 1986, and 1991), and the University College London Medical School (UCLMS) Cohort Study (entered clinical studies in 2005 and 2006). Different studies had different outcome measures, but most had performance on basic medical sciences and clinical examinations at medical school, performance in Membership of the Royal Colleges of Physicians (MRCP(UK)) examinations, and being on the General Medical Council Specialist Register. Results: Correlation matrices and path analyses are presented. There were robust correlations across different years at medical school, and medical school performance also predicted MRCP(UK) performance and being on the GMC Specialist Register. A-levels correlated somewhat less with undergraduate and post-graduate performance, but there was restriction of range in entrants. General Certificate of Secondary Education (GCSE)/O-level results also predicted undergraduate and post-graduate outcomes, but less so than did A-level results, but there may be incremental validity for clinical and post-graduate performance. The AH5 had some significant correlations with outcome, but they were inconsistent. Sex and ethnicity also had predictive effects on measures of educational attainment, undergraduate, and post-graduate performance. Women performed better in assessments but were less likely to be on the Specialist Register. Non-white participants generally underperformed in undergraduate and post-graduate assessments, but were equally likely to be on the Specialist Register. There was a suggestion of smaller ethnicity effects in earlier studies. Conclusions: The existence of the Academic Backbone concept is strongly supported, with attainment at secondary school predicting performance in undergraduate and post-graduate medical assessments, and the effects spanning many years. The Academic Backbone is conceptualized in terms of the development of more sophisticated underlying structures of knowledge ('cognitive capital’ and 'medical capital’). The Academic Backbone provides strong support for using measures of educational attainment, particularly A-levels, in student selection