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Predicting college success: The importance of ability and non-cognitive variables
Abstract
This chapter introduces a study which focuses on predicting college success as measured by students’ grade point averages (GPAs). The chapter also reviews prior research related to various types of predictors. Specifically, two categories of predictors are identified: ability measures and non-cognitive variables. Finally, an overview of the study is presented.
... While it is a widely accepted inference that past achievement predicts future success, the composite variable of HSGPA offers little explanatory power as to how or why it facilitates the adjustment of transitioning students and is likely influential because it represents an amalgamation of personal and environmental factors (Noble & Sawyer, 2004;Sawyer, 2007;Schmitt et al., 2009). Noting that HSGPA often accounts for up to 30% of the variability in postsecondary achievement and retention models, predictive accuracy could be improved by identifying additional variables and deconstructing the specific factors that make HSGPA influential during this time (Astin, 1993;Johnson, 1997;Ransdell, 2001). ...
... Specifically, the reproducibility of the theoretical factor structure has been seldom confirmed and numerous reconfigurations have been proposed to replace the original conceptual model (see Gucciardi et al., 2011 for review). On a related note, the factors examined by the CD-RISC do not align with factors outlined within contemporary academic resilience literature and, as such, it is not surprising that low correlations were demonstrated between general resilience factors and post-secondary success (e.g., Adebayo, 2008;Johnson, 1997;Perez et al., 2009;Ransdell, 2001;Schunk, 1991). Luthar, Cicchetti, and Becker (2000) argue that resilience should be measured with context-specificity and recognize that individuals who may present as resilient to one challenge can be broken by another. ...
... Test) and demographic information (e.g., ethnicity; Adebayo, 2008;Johnson, 1997;Ransdell, 2001). The results of this line of research appeared limited, with high school achievement appearing as one of the most salient predictors of post-secondary success. ...
Entry into post-secondary studies from high school presents students with an assortment of challenges that extend far beyond conventional academic demands. These students are often required to modify their orientations to learning, foster, and maintain new social support networks, manage complex responsibilities, regulate personal freedoms, and navigate the many environmental and psychological stressors that will likely appear along the way. In the empirical literature, there is consensus that high school grade point average (HSGPA) represents the best predictor of success during this time of transition. Although HSGPA is often used to screen for entrance into higher education, little is known as to how or why it facilitates positive adjustment. Some have argued that HSGPA is influential because it is represents an interaction of internal and external factors. The purpose of this study was to undertake an empirical investigation to determine whether academic resilience, as defined by specific traits found within students that help them overcome situational adversity to achieve academically, mediates the relation between HSGPA and post-secondary success (i.e., achievement and retention outcomes). Using structural equation modeling, the responses from 655 first-year undergraduate participants were examined and demonstrated positive findings. Specifically, academic resilience accounted for approximately 5% of shared variance between HSGPA and post-secondary academic achievement. Due to the limited number of students who identified themselves as leaving academic studies, the mediational properties of academic resilience could not be explored within the context of post-secondary retention. The limitations of this study and future directions are discussed in relation to the obtained results.
... While it is a widely accepted inference that past achievement predicts future success, the composite variable of HSGPA offers little explanatory power as to how or why it facilitates the adjustment of transitioning students and is likely influential because it represents an amalgamation of personal and environmental factors (Noble & Sawyer, 2004;Sawyer, 2007;Schmitt et al., 2009). Noting that HSGPA often accounts for up to 30% of the variability in postsecondary achievement and retention models, predictive accuracy could be improved by identifying additional variables and deconstructing the specific factors that make HSGPA influential during this time (Astin, 1993;Johnson, 1997;Ransdell, 2001). ...
... Specifically, the reproducibility of the theoretical factor structure has been seldom confirmed and numerous reconfigurations have been proposed to replace the original conceptual model (see Gucciardi et al., 2011 for review). On a related note, the factors examined by the CD-RISC do not align with factors outlined within contemporary academic resilience literature and, as such, it is not surprising that low correlations were demonstrated between general resilience factors and post-secondary success (e.g., Adebayo, 2008;Johnson, 1997;Perez et al., 2009;Ransdell, 2001;Schunk, 1991). Luthar, Cicchetti, and Becker (2000) argue that resilience should be measured with context-specificity and recognize that individuals who may present as resilient to one challenge can be broken by another. ...
... Test) and demographic information (e.g., ethnicity; Adebayo, 2008;Johnson, 1997;Ransdell, 2001). The results of this line of research appeared limited, with high school achievement appearing as one of the most salient predictors of post-secondary success. ...
Resilience is often conceptualized as a unidimensional construct that is composed by intrinsic and extrinsic factors that have the ability to augment or hinder an individuals’ likelihood of overcoming challenges (Kaplan, 1999). While modeling resilience in this manner has proven useful in the past, many researchers have argued that such an approach has been fundamentally flawed (e.g., Masten, Best, & Garmezy, 1990). Recognizing this argument, constructs such as academic resilience were created to offer greater assessment and prediction specificity to resilience research. Academic resilience can be defined as the increased likelihood of educational success despite personal adversities or vulnerabilities brought on by environmental conditions. Over the years, research into the construct of academic resilience has primarily occurred secondary learning level and is reflected in our contemporary measurement practices (Martin and Marsh, 2006). The purpose of this study is to examine the psychometric rigor of Martin and Marsh’s (2006) well-used academic resilience scale to determine how well this contemporary instrument functions within the post-secondary learning environment. The results of this study will provide suggestions for model and scale revisions, and lay important groundwork towards the development of an academic resilience scale intended to survey post-secondary populations.
... In fact, it has been established that "Intelligent Quotient (IQ) accounts for only 20 percent of the factors that determine success in life" (Chemiss & Goldman, 1999, p. 26). And that many of the most successful people in academic, business and social world have a high degree of what has become known as Emotional Intelligence (EQ) Parker, Summerfeldt, Hogan & Majeski 2003;Ransdell, 2001). ...
... It is clear by the findings of the present study that EQ skills are learnable skills and since there is ample research evidences Parker, Summerfeldt, Hogan & Majeski, 2003;Ransdell, 2001;Salovey, Bedell, Detweiter & Mayer, 2000) pointing to the fact that human behaviour and achievements are directly influenced by their levels of emotional learning competencies. What is reasonable for any government to do to enhance social and personal development of its citizenry, is to encourage the teaching and learning of EQ skills as early as elementary school stage. ...
Achieving the kind of balance that encourages all children to learn, work, and contribute to their fullestpotential has been a continuing challenge as the world grows more complex and our communitiesenveloped with various challenges of socio-political and economic disintegration. With rapid changesin technology and global competition in all facets of human endeavour, it is more crucial than ever thatadolescents are fully equipped to compete for knowledge and technology based jobs. When studentsare not well prepared for the challenges ahead most especially from the secondary school stage ofeducation, the cost to individuals and the implication to the society can better be imagined. Forexample, the transition from high school to university is very stressful for most individuals (McLaughlin,Brozovsky & McLaughlin, 1998; Perry, Hladkyj, Pekrum & Pelletier, 2001; Pratt et al, 2000). Themajority of high school students who go on to post-secondary institutions withdraw before graduation(Gerdes & Mallinckrodt, 1994; Pancer, Hunsberger, Pratt & Alisat, 2000). First-year university studentsface a variety of stressors: making new relationships, modifying existing relationships with parents andfamily (e.g. living apart), and learning to cope with the new academic environment. Furthermore, theymust learn to function as independent adults (e.g. budgeting time and money). Failure to master thesefamiliar tasks appears to be the most common reason for undergraduate students withdrawing fromuniversity (Blanc, DeBuhr & Martin, 1983).
... 581). Another study conducted by Ransdell (2001) focused on predicting college success as measured by grade point averages (GPAs). The findings suggested two categories of predictors as follows: ability measures (including verbal and quantitative ability) and noncognitive variables (including interest in school, willingness to study, persistence, time spent on outside of school activities and encouragement from parents). ...
... Teaching with emotional intelligence (EI) can have beneficial results for both the professor and the students (Ransdell, 2001;Haigh and Clifford, 2011;Song et al., 2010). Being empathic and active listeners, professors may create a genuine communication with their students. ...
Purpose
This study examines the role of emotional intelligence on academic achievement among students at a private university in Kuwait.
Design/methodology/approach
The data were obtained through a questionnaire which elicits information on students' sociodemographic data and their overall college grade point average (GPA). The 16-item Wong and Law Emotional Intelligence Scale (WLEIS, Wong and Law, 2002), was used to evaluate the level of emotional intelligence and explore the effect on academic performance in a sample of 480 Kuwaiti college students.
Findings
The results of the study indicate that academic success was strongly associated with self-emotion appraisal (SEA) and use of emotions (UOE). However, the results did not show direct correlations with age, high schooling system, gender and nationality. Additionally, results provide supporting evidence that the WLEIS scale has good psychometric properties and can be used as a reliable tool to assess the emotional intelligence skills among college students in Kuwait.
Research limitations/implications
The study has several limitations that require consideration when interpreting the findings. First, this research used a quantitative methodology, which can provide limited information about emotional intelligence, and further qualitative research is necessary to identify contributors and inhibitors of this construct. Second, as in any study using self-report measures, the results may have been influenced by participants' acquiescence and need for social desirability. Further studies should aim to include ways in which EI can be incorporated into academic curricula and qualification framework and barriers that may pertain to encourage emotional intelligence skills development in higher education and suggest solutions accordingly. In future studies it would be interesting to see educators' self-perception vs of students to include a multi-rated for the emotional intelligence. To this end, these areas of study could provide a more comprehensive understanding in the sense of integrating emotional intelligence theories and methods from multiple disciplines that constitute social, personality and psychological trait within higher education. This research has only considered samples from a private university in Kuwait. Extension of sampling scope to other universities around the country and in the Middle East may bring a better understanding of students' emotional intelligence level. In terms of EI components, the results of this study indicated that students score highest in self-emotional appraisal (SEA) and the use of emotions (UOA) and lowest on regulation of emotions (ROE). Additional studies can be conducted to see whether the same results apply on Arab students in the Middle East as a whole. The present study has provided more evidence of the need for cross-cultural comparison of an imported construct and its measurement by showing that the emotional intelligence construct, defined by the WLEIS (Wong and Law, 2002), may be understood differently in other cultures.
Practical implications
There are two key implications in this study, one concerning gender and the other relating to students' GPA. The results suggested differences between the way female and male students viewed EI skills in relation to their academic achievement. Considering that the instrument used to measure EI was the Wong and Law Emotional Intelligence Scale (WLEIS), a self-report measure, perhaps a degree of bias was introduced. Male students' EI scores as a whole ( M = 5.56) were higher than the EI mean score for female students ( M = 5.39). As Novinger (2001) proposed, emotional expressiveness in the Arab world is such that women are trained to be less demonstrative of their emotions than men.
Social implications
In addition, gender and cultural values may influence communication styles among Arab students during the teaching process. An awareness of gender and cultural difference related to EI could be beneficial to all parties (students, educators and administrators) in higher educational institutions. Educators' sensitivity to students' EI skills associated with culture can be manifested in a wide variety of teaching practices, ranging from educators' expectations toward students to their interpersonal interactions with students and from teaching styles to assessment methods. For example, an understanding of the possible impact of gender on EI skills may raise educators' levels of cultural sensitivity in dealing with students in the Middle East, particularly, in Kuwait. Even though this study did not show a significant relationship between the overall EI level and students’ GPA, an effect on EI components SEA and UOA was found. University administrators and educators wishing to increase students' academic achievement would do well to incorporate the use and recognition of emotions into their curricula. For instance, emotions can be used to channel the anxiety created by exams to motivate students to prepare more thoroughly and attain more higher standards.
Originality/value
Emotional intelligence skills are important predictors of academic success, and they play a key role in students' performance, and greater the emotional intelligence, the academic achievement will be higher. The results of this study support the research studies suggesting that students' emotional intelligence (EI) should be considered by curriculum designers to enable educators assist their students reach successful academic performance.
... Many studies [7][8][9][10][11][12][13][14][15][16][17][18] using educational DM algorithms have been conducted which can identify at risk students by predicting course outcomes, for example, from their forum activities, content request, and time spent online. None of these studies used in-class participation to predict the course outcomes despite their emphasis on class interaction [19][20][21][22][23]. ...
... Term prediction in this study means using statistical techniques and DM algorithms on the observation to characterize the model. In many academic disciplines prediction has been made on the students' performance data [7][8][9][10]. Different DM techniques have been used in EDM to make predictions. ...
... The general perception is that a negative correlation exists between time spent on study outside formal classes and academic performance (Nonis & Hudson, 2006; Randsell, 2001). However, the specific nature of this relationship is complex (Lammers et al., 2001; Valentine et al., 2002). ...
... Overall, hypothesis 1 (H1) has been supported by these results. The time students spend on their studies and also their motivation levels are both predictors of academic success (Fairchild et al., 2005; Lammers et al., 2001; Randsell, 2001; Walker et al., 2006). Because of the increasing numbers of working students and the significant effect that working has on study time (Lammers et al., 2001), testing for differences between working and non-working students in this sample in regard to their motivation levels, specifically intrinsic motivation or self-determination (H2), would provide useful additional information.Fig. ...
Considered essential to lifelong learning, information literacy skills and information literacy self-efficacy are associated with higher levels of student academic motivation. However, little is known about the interrelationships between the different types of academic motivation and information literacy self-efficacy. This study investigates the relationships between these constructs. Data were collected using a questionnaire comprising existing scales. The questionnaire was administered to undergraduate students in an Australian higher education institution with a response rate of 58%, resulting in 585 completed questionnaires. Both intrinsic and extrinsic academic motivation were found to be positively related to information literacy self-efficacy, while amotivation was negatively related. The most important predictor of information literacy self-efficacy was intrinsic motivation to know. Overall, all academic motivation types increased over time, including, unexpectedly, amotivation. Differences were apparent by gender. The need for higher education institutions to actively identify academically amotivated students and facilitate intrinsic academic motivation is discussed.
... The general perception is that a negative correlation exists between time spent on study outside formal classes and academic performance (Nonis & Hudson, 2006;Randsell, 2001). However, the specific nature of this relationship is complex (Lammers et al., 2001;Valentine et al., 2002). ...
... The time students spend on their studies and also their motivation levels are both predictors of academic success (Fairchild et al., 2005;Lammers et al., 2001;Randsell, 2001;Walker et al., 2006). Because of the increasing numbers of working students and the significant effect that working has on study time (Lammers et al., 2001), testing for differences between working and non-working students in this sample in regard to their motivation levels, specifically intrinsic motivation or self-determination (H2), would provide useful additional information. ...
Information literacy self-efficacy and academic motivation are both argued to play important roles in student academic development. The former is considered to be a predictor of student academic achievement while the latter is considered a key factor in developing information literacy self-efficacy. Today, many students undertake paid employment in conjunction with their academic studies and little is known about the effect this may have on their information literacy self-efficacy and academic motivation. As such, the relationship between information literacy self-efficacy, academic motivation, and employment has been unexplored. Data were collected via a questionnaire, comprised of existing scales, which was administered to undergraduate business students in an Australian higher education (HE) institution. A response rate of 58% resulted in 585 completed questionnaires. Findings suggest that whether or not students were engaged in paid employment did not appear to influence information literacy self-efficacy, although students in paid employment did exhibit significantly lower intrinsic motivation than students not in paid employment. Additionally, for students not in paid employment a significant relationship was found between amount of time spent on study and information literacy self-efficacy. Of some concern, the small amount of time students reported spending in academic pursuits outside of scheduled classes raises issues regarding the placement of information literacy instruction. For information literacy practitioners this study contributes to awareness regarding the conceptualization of information literacy instruction and its placement in the HE environment.
... Predicting student academic performance has long been an important research topic in many academic disciplines (e.g., Cohen, Manion, & Morrison, 2007;Grudnitski, 1997;Pokay & Blumenfeld, 1990;Ransdell, 2001;Ting, 2001). Based on the results of a predictive model, the instructor can take proactive measures (Veenstra, Dey, & Herrin, 2008;Ware & Galassi, 2006) to improve student learning, especially for those lowperformance students. ...
... First, the predictive models developed in the present study only take into account eight cognitive factors (X 1 to X 8 ). A significant amount of research (e.g., Graaff, Ransdell, 2001;Riding & Rayner, 1998;Tracey & Sedlacek, 1984) has suggested that learning is an extremely complex process involving many psychological factors such as learning styles, self-efficacy, achievement goals, motivation, interest, and teaching and learning environment. These psychological factors will be considered in our future modeling work to develop a more accurate predictive model. ...
Predicting student academic performance has long been an important research topic in many academic disciplines. The present study is the first study that develops and compares four types of mathematical models to predict student academic performance in engineering dynamics – a high-enrollment, high-impact, and core course that many engineering undergraduates are required to take. The four types of mathematical models include the multiple linear regression model, the multilayer perception network model, the radial basis function network model, and the support vector machine model. The inputs (i.e., predictor variables) of the models include student's cumulative GPA, grades earned in four pre-requisite courses (statics, calculus I, calculus II, and physics), and scores on three dynamics mid-term exams (i.e., the exams given to students during the semester and before the final exam). The output of the models is students' scores on the dynamics final comprehensive exam. A total of 2907 data points were collected from 323 undergraduates in four semesters. Based on the four types of mathematical models and six different combinations of predictor variables, a total of 24 predictive mathematical models were developed from the present study. The analysis reveals that the type of mathematical model has only a slight effect on the average prediction accuracy (APA, which indicates on average how well a model predicts the final exam scores of all students in the dynamics course) and on the percentage of accurate predictions (PAP, which is calculated as the number of accurate predictions divided by the total number of predictions). The combination of predictor variables has only a slight effect on the APA, but a profound effect on the PAP. In general, the support vector machine models have the highest PAP as compared to the other three types of mathematical models. The research findings from the present study imply that if the goal of the instructor is to predict the average academic performance of his/her dynamics class as a whole, the instructor should choose the simplest mathematical model, which is the multiple linear regression model, with student's cumulative GPA as the only predictor variable. Adding more predictor variables does not help improve the average prediction accuracy of any mathematical model. However, if the goal of the instructor is to predict the academic performance of individual students, the instructor should use the support vector machine model with the first six predictor variables as the inputs of the model, because this particular predictor combination increases the percentage of accurate predictions, and most importantly, allows sufficient time for the instructor to implement subsequent educational interventions to improve student learning.
... Research by psychologists and education theorists indicates that high school grades and scores in tests such as the SAT and the ACT are -however imperfect -measures of students' cognitive (e.g. verbal and mathematical) abilities which are important conditions for their performance during their academic studies (Linn, 1989;Ransdell, 2001;Wolfe & Johnson, 1995). Also, the work by Robinson & Monks (2005), who show that students who withheld their SAT results in the context of admission procedures had a lower average GPA than those who submitted their standardized test results, suggests that there is some value in the SAT score to colleges in the context of admission decision. ...
... The first factor primarily measures the mathematical-analytical abilities of the individuals in the study. The second factor captures the individuals' verbal abilities (Ransdell, 2001;Ransdell, Hawkins & Adams, 2001), and may also reflect their interactive skills. Both factors may also, to some extent, measure aspects that are conceptually distinct from ability, such as motivation and neatness (Rose & Betts, 2004). ...
In this article, the author analyzes the intertemporal consistency of high school grades as predictors of the academic performance of business administration students over a 2-year period by using data from a university in Germany. This study shows how students' average high school grades and a range of other factors are regressed on the students' grade performance at regular half-year intervals during their participation in the program. The author applies a bootstrapping procedure to analyze changes in the regression estimators over time and finds that the magnitude of the coefficients on high school grades decreases over the 2-year period, and this decline is statistically significant. Nevertheless, high-school grades remain the most important predictors of the students' performance throughout the period studied here.
... It is important to note that academic success should not be the sole measure of a student's worth or potential. Each student is unique, and success can be defined in various ways beyond academic achievements (Ransdell, 2001). Various studies have explored the relationship between personality traits and academic performance, and some consistent findings have emerged. ...
Background
The current operational military environment is changing, complex, unpredictable, and ambiguous. Due to such situations, soldiers are constantly forced to think about their values, norms, and roles that should be part of their profession. Consequently, they must first be educated and trained on how to behave in a particular operational military environment. Pursuing an officer’s education at military academies is very difficult not only physically but also psychologically. Cadets are required to be prepared to lead in extreme environments upon graduation. Despite the fact that military tasks are technically complex, the individual operational activities of soldiers are gaining more and more strategic meaning. Therefore, the importance of selecting the process and military education programs of soldiers is increasingly stressed. Cognitive abilities and skills individually predict performance in academic and professional settings, but it is less clear how personality can influence performance. Therefore, this study focused on the explanation of the individual factors that affect the achievements of the cadets. Specifically, the objective of this study was to examine direct and mediated relationships between personality traits and the military and academic performance of cadets.
Methods
This study followed a quantitative method analysis. The research models were assessed using the structural equation modeling technique. Bootstrap was applied to evaluate a 95% level confidence interval on estimates with 5,000 bootstrap samples, and to evaluate direct and indirect effects. The analysis was based on a sample of 120 cadets from the Lithuanian Military Academy. The effects on military and academic performance were evaluated using the Self-Efficacy scale, the Big Five personality trait scale, academic performance was evaluated through academic grades and military performance was evaluated using instructor ratings.
Results
To support our hypotheses, it was found that self-efficacy has a mediating effect on the performance of cadets. Additionally, the traits of conscientiousness, openness to experience and extraversion were related to both military and academic performance. Furthermore, self-efficacy appeared as a partial mediator of the relationship between personality traits and cadet performance.
Conclusion
The findings of this study help clarify the relationship between the personality traits of the cadets and the military and academic performance. In addition, these results may be useful for the further development of military education and training, for the development of testing, and selection of military personnel.
... Thus, feedback supposedly encourages students to take further actions for continued improvement. Motivational beliefs are proximal antecedents of students' achievement goals [12][13][14], and the two jointly affect students' adoptions of adaptive cognitive and metacognitive strategies [15,16], resulting in varied academic performance [17,18]. Consequently, it is vital to understand whether student conceptions of the nature and purpose of feedback would support more desirable motivation and goal orientations beyond contributing to academic performance. ...
Extant research on students’ feedback conceptions has reported effects on performance, but the relationship of feedback conceptions to important motivational factors is not empirically evidenced. This study fills this gap by providing empirical data about students’ conceptions of feedback in relation to their motivational beliefs and achievement goals. Measurement and structural modelling analyses were based on undergraduate student data from New Zealand domestic (n = 832) and Chinese (People’s Republic of China, PRC) international students (n = 504) in New Zealand universities. Based on cross-ethnic invariant measurement models of conceptions of feedback, motivational beliefs, and achievement goals, a structural equation model uncovered links between feedback conceptions, motivational beliefs, and achievement goals. Specifically, feedback conceptions believing in actively using feedback and the value of teacher comments significantly promoted self-efficacy and task value beliefs, as well as mastery and performance-approach goals. In contrast, maladaptive feedback perceptions (i.e., feedback is ignored or used for judging performance against external standards or relative to others) had a minimal-to-negative impact on motivational beliefs and triggered stronger performance-avoidance goals. This study empirically demonstrates that conceptions of feedback support motivational beliefs and goal approaches consistent with previous claims concerning their self-regulatory role.
... Research regarding the influence of gender on college retention, however, is inconsistent (Alarcon & Edwards, 2013;Ishitani, 2016). Studies examining the effect of race on college GPA and retention have found that minority students are more likely to have a lower GPA and drop out from college more often than non-minority students (Murtaugh et al., 1999;Ransdell, 2001). Research regarding the influence of socioeconomic status (SES) on college academic performance and retention is mixed, with some showing poorer academic outcomes for students from low SES backgrounds, while other studies show no effects of SES (Baier et al., 2016;Pritchard & Wilson, 2003). ...
As the number of college students with disabilities continues to grow, self-advocacy programs have become an increasingly important tool to help students access disability-specific and general student services. Yet, few studies have explored processes surrounding the development of self-advocacy for students with dis-abilities, in particular the role of social support in fostering important knowledge and skills. In this study, we conducted semi-structured interviews with 28 students receiving disability services (DS) from a large Hispanic serving institution (HSI). Our analysis yielded findings related to four subcomponents of self-advocacy, (a) knowledge of self, (b) knowledge of rights, (c) communication, and (d) leadership. Students attributed formal and informal social support to their progression in each area, distinguishing between initial and advanced phases of self-advocacy development. Recommendations for future research and implications for secondary and postsecondary education are provided.
... Table 3 and 4 and 5 above showed that there were significant differences in the post innovation competence of participants, hence, the Null hypothesis as stated above was rejected. This finding was consistent with earlier studies by Akindele (2006), Akinboye (2003), De-Bono (1972), Gordon (1996) , Nwazuoke (2004) and Randsell (2001) that found that trained participants' were superior and more innovative than untrained participants'. More importantly, the finding that managers can be assisted through training to be more innovative has helped to correct a number of erroneous believes about creativity and innovation. ...
The overall intent of this research study is to investigate the relative effect of SIMPLEX and Akinboye Practical Creativity at Work (APCAW) strategies on the business innovation competence of selected managers in Information Technology and Service industry in Lagos. The study adopted a pre-test, post-test, control group, experimental design with a 3x3x2 factorial matrix. The participants consisted of 126 managers drawn from three ITS companies in Lagos, Lagos state. The participants were categorised according to their levels of emotional intelligence based on their score on the Shutte emotional intelligence scale. Thereafter, the experimental and control groups were exposed to six weeks of Simplex (foreign) and Apcaw (indigenous) and placebo training respectively. The dependent variable was business innovation competence. This was measured using the business innovation assessment scale (BIAS). One hypothesis was tested and the data was analysed using Analysis of Co-variance (ANCOVA), pair wise comparison. Findings from the study revealed that treated participants' were significantly superior to control on business innovation competence (F(2,107)=12.304;p<0.00). Conclusively, SIMPLEX and APCAW techniques were found to be effective in fostering business innovation competence of managers. It was therefore recommended that management should employ the use of these techniques to enhance employee business innovation competence on the job. In addition, employers of labour are strongly encouraged to incorporate creativity and innovation training in their personnel programme, such as, in the induction, recruitment, selection and promotion of staff to managerial cadre. .
... However, some essential elements for knowledge creation are not required for active learning and EL theory states that learning takes place when learners analyze, interpret, and make use of knowledge (McCarthy, 2010). Numerous studies examined the relationships between grit and cognitive or non-cognitive issues like educational achievement or personality traits (Ransdell, 2001). For instance, the positive connections exist between grit and grade point average (GPA) and accomplishment (Chen et al., 2015;McDermott et al., 2015), and completion of homework (Bennett et al., 2013). ...
Students' success as a cognitive issue in learning is prejudiced by proper learning approaches which improve their comprehension and achievement. In an attempt to scrutinize supplementary or alternate variables that envisage students' success, the researcher inspected a non-cognitive factor, namely grit, theorized as passion and perseverance due to its long-term quality, on the one hand, and its popularity among scholars in preceding decades on the other hand. Moreover, experiential learning (EL) is a momentous instructional approach used in the educational process to accelerate “do it and learn.” The proposed review aims to gauge the EL approach as well as grit to regulate learners' educational success. Consequently, some pedagogical implications are presented for teachers, students, and syllabus designers.
... In addition, measures intended specifically for this purpose have been developed, such as the Noncognitive Questionnaire (Sedlacek, 2004;Tracey & Sedlacek, 1989) and a biographical inventory (Oswald, Schmitt, Kim, Ramsay, & Gillespie, 2004;Schmitt et al., 2007). However, the value of various noncognitive variables continues to be debated (Lotkowski, Robbins, & Noeth, 2004;Ransdell, 2001;Thomas, Kuncel, & Crede, 2007;Wolf & Johnson, 1995). According to a meta-analysis conducted by Robbins et al. (2004), the best non-cognitive predictors of college GPA are academic self-efficacy (r = .50) ...
Motivation and, inferentially, commitment are critical, non-cognitive factors in college success. One needs to detect and measure these attributes prior to a student's acutal enrollment in classes since early detection of at-risk students can lead to the most productive intervention initiatives. Freshmen entering into La Salle University were required to complete a form used as a basis for advising. Students complying (n=427) and not complying (n=291) with the request were compared on high school grade point average (GPA), SAT scores, and first-term college GPA. The noncompliant students had lower credentials on the admissions criteria (high school GPA, SAT) as well as on the outcome measure (first-term college GPA), although the effect sizes were small. The findings support the contention that compliance with requirements is a proxy for academic motivation and can serve as a cue to how well a student will perform.
... GPA. GPA is used as an overall measure of academic success and plays a crucial role in the college admission decision-making process (Kobrin & Patterson, 2011) . After students enter college, GPA continues to play an important role as an overall representation of their current academic standing (Ransdell, 2001) . Most higher education institutions use GPA as a tool for evaluating students and determining criteria for academic probation . ...
This study explored whether and to what extent vocational personality types based
on Holland (1994) correlate with and explain unique variance of academic success
among 117 undergraduate civil engineering students by using the Self-Directed
Search–Form R, 4th Edition. Findings indicated that the majority of participants’
1st-letter code was Realistic (39.3%), 2nd-letter code was Investigative (24.8%),
and 3rd-letter code was Social (21.4%), compared with Holland’s 3-letter codes for
civil engineering (Investigative, Realistic, Enterprising). The study also calculated
the degree of congruence between personality type and occupational environment.
For career counselors, the findings underscore the importance of using congruence
scores to predict engineering students’ academic performance.
... Student evaluation techniques and their relationship to grades have been a discussion topic for many decades in various academic disciplines (Huang & Fang, 2013;Ransdell, 2001;Ting, 2001). Relating evaluation to grades enables educators to take proactive measures in the classroom, for example, changing instruction, reviewing lecture materials and assignments, providing extra resources to students, and setting up prerequisites courses (Huang & Fang, 2013). ...
Background
Computer-aided learning management systems (LMSs) are widely used in higher education and are viewed as beneficial when transitioning from conventional face-to-face teaching to fully online courses. While LMSs have unique tools for transferring and assessing knowledge, their ability to engage and assess learners needs further investigation. This paper focuses on a study examining the LMS “Moodle” to ascertain the effectiveness of “Moodle quizzes” to improve, assess and distinguish knowledge in a civil engineering course at an Australian university. The course has a database comprising 62 formative and 61 summative quiz questions with embedded text, images, audio and video. This study investigates the use of these quiz questions with four course cohorts and 169 students. The quizzes assessed competencies of students during various stages of a study period through automated marking. The suitability of questions to assess and distinguish student knowledge levels was determined using a psychometric analysis based on facility index (FI) and the discrimination index (DI) statistics embedded within the Moodle quizzes.
Results
This study highlights strategies used to set and review quiz questions for formative and summative assessments. Results indicated that students were engaged and satisfied in the formative assessment because they viewed the interactive videos between 2 and 6 times and 65% of students attempted all the formative questions. The FI indicated student pass rate for the summative questions and DI indicated the difficulty of these questions, while the combination of FI and DI results separated students with different knowledge levels. Using these Moodle statistics provided information to make effective decisions on how to improve the summative quizzes.
Conclusion
The multimodal quizzes were effective in teaching and assessing a theoretical engineering course and provided efficient methods to replace conventional assessments. The FI and DI indexes are useful statistical tools in redesigning appropriate sets of questions. Time-poor academics will benefit from using these easily attainable Moodle statistics to inform decisions while revising the quizzes and making assessments more autonomous.
... Although the relationship between previous school performance or other measures of cognitive abilities (such as intelligence tests) and academic success have often been investigated (Erdel, 2010;Farsides & Woodfield, 2003;Giese et al., 2013), the results remain rather inconclusive: previous school performance is either seen as a valid predictor of academic success (e.g. Murtaugh et al., 1999) or the validity of previous school performance as a predictor is disputed as it does not account for much variability in academic success or retention (Berger & Milem, 1999;Johnson, 1997;Ransdell, 2001). For instance, a review of studies regarding the validity of the SAT and high school grades in the American education system showed that the predictive validity ranged between r = .36 ...
... Much of the early efforts and research focused on: Parental background; impact of previous school performance (that is, pre-school and elementary school marks); students attitude to assignments; and cognitive abilities/factors (Tinto, 1993;Adesemowo, 1990;Parker, et al, 2004b). However, these variables were found to account for relatively small amount of the variability in academic success or student attrition (Berger & Milem, 1999;Mayer & Salovey, 1997;Randsell, 2001). Later, researcher turned their attention to a broad range of other possible predictors for academic achievement in secondary school students. ...
The study was to investigate beneficiaries’ perception of the poverty alleviation programmes of the Federal Government of Nigeria with a focus on Akwa Ibom State. Though poverty is a worldwide phenomenon, Nigeria’s poverty case is pathetic and difficult to comprehend judging from the backdrop of Nigeria’s abundance in human and natural resources. Government’s concern and desire to alleviate poverty led to the introduction of the current Poverty Alleviation Programmes to improve living standards of beneficiaries. Three hypotheses were postulated to guide the study. A 20 item structured questionnaire was developed to elicit information from the beneficiaries. Two hundred and forty respondents expressed their views. The responses from the subjects were coded and analyzed using chisquare. Results showed that there is no significant effect of poverty alleviating programme on the economic empowerment of the beneficiaries in Akwa Ibom State. Recommendations based on the results were made so as to ensure effective exercise towards alleviating poverty in Nigeria.
... Márquez-Vera et al (2013) have divided these features into general survey and specific survey as shown in Table 1. Even though socioeconomic and psychological factors (non-cognitive attributes) play a big role in academic performance (Ransdell, 2001), sometimes it may be difficult to get these noncognitive data. Data such as the quality of teaching could be gotten through quantitative surveys, but answers to questions such as "rate the quality of teaching on a scale from 1 to 5" will always be subjective, thus making it harder to repeat experiments. ...
Optimal student performance is integral for successful higher education institutions. The consensus is that big data analytics can be used to identify ways for achieving better student academic performance. This article used support vector machines to predict future student performance in computing and mathematics disciplines based on past scores in computing, mathematics and statistics subjects. Past subjects passed by students were ranked with state of art feature selection techniques in an attempt to identify any connection between good performance in a particular discipline and past subject knowledge. Up to 80% classification accuracy was achieved with support vector machines, demonstrating that this method can be developed to produce recommender or guidance systems for students, however the classification model will still benefit from more training examples. The results from this research reemphasizes the possibility and benefits of using machine learning techniques to improve teaching and learning in higher education institutions.
... Whenever students are engaged with technologies, they leave a footprint, which is collected through the tools including Learning Management Systems. Different studies used the data only from learning management systems in order to identify at risk students [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28]. However, these studies lack in-class participation data in spite of the fact that different theories of learning emphasized the importance of participation in classes. ...
... One of the main lines of investigation has been the prediction of student performance (West, Heath & Huijser, 2015). This has been an active and relevant topic because traditional grade-based evaluations are currently the most widespread approach to measuring and monitoring the learning process (McKenzie & Schweitzer, 2001;Ransdell, 2001;Arnold & Pistilli, 2012). This line of research has been addressed in EDM from various perspectives: early identification of at-risk students, which allows for timely counselling as well as coaching to increase student success rate and retention (Vandamme, Meskens & Superby, 2007;Macfadyen & Dawson, 2010;Falkner & Falkner, 2012;Marbouti, Diefes-Dux & Madhavan, 2016); student dropout detection and reduction, which is especially popular for online courses, which tend to have high dropout rates (Cocea & Weibelzahl, 2006; Lykourentzou, Giannoukos, Nikolopoulos, Mpardis & Loumos, 2009); ...
An increasing number of higher education institutions have deployed learning management systems (LMSs) to support learning and teaching processes. Accordingly, data-driven research has been conducted to understand the impact of student participation within these systems on student outcomes. However, most research has focused on small samples or has used variables that are expensive to measure, which limits its generalizability. This article presents a prediction model based on low-cost variables and a sophisticated algorithm, to predict early which students attending large classes (with more than 50 enrollments) who are at risk of failing a course. Therefore, it will enable instructors and educational managers to carry out early interventions to prevent course failure. The results overperform other approaches in terms of accuracy, cost, and generalization. Moreover, LMS usage information improved the model by up to 12.28% in terms of root-mean-square error, enabling better early identification of at-risk students.
... Although much of the early work Several studies on achievement at university have focused on the impact of previous school performance reached similar conclusions. In study with a large sample, (high-school marks) and/or standardized measures o f after controlling the high school achievement it is found cognitive abilities, the predictive power of these types of that non-cognitive factors have impacts on university variables were quite limited [2,3]. entrance [6]. ...
The aim of the study is to analyse the achievement levels of undergraduate students attending to the faculty of education based on the type of the high schools they graduated from and their order of preferrence at the University Placement Examination. The sample of the study includes a total of 1957 undergraduate students attending to several departments within the faculty of education such as Turkish language education, computer and instructional technology education, early education, social sciences education, sciences education and classroom teaching. 1130 of the subjects are females whereas 827 are males. The study concludes that the graduates from the Anatolian Teacher higher schools attend to the departments as their high order of preferrences (n:217, x:6.43). However, in terms of achievement levels, they have the lowest level of achievement. (x:2.19). The achievement level of the graduates of high schools with foreign language focus appear to be the highest (n:377, x:2.69). It is also determined that there is a negative relationship between the order of preferrence and their level of achievement but this relationship is not significant.
... Several researchers (DeBerard, Spielsmans, & Julka, 2004;Ensign & Woods, 2014;Huang, 2011;Messick, 1979;Randsdell, 2001;Richardson, Abraham, & Bond, 2012;Fenollar, Roman, & Cuestas, 2007) try to uncover the multidimensionality of academic success by investigating different non-cognitive variables such as past educational life, coping strategies, social/family support, quality of one's life, one's efforts to be successful, degree of dedication to educational process, participation to courses, academic self-efficacy, achievement motivation, degree of responsibility, ability of time management, ability to identify proper vocational goals and effective learning strategies, study quality and length, self-concept, creativity, socio-economic status and so on. As university students need powerful psychological resources promoting their academic success and enabling them to survive in a competitive business world (Luthans, Luthans, & Jensen, 2012), psychological capital, academic confidence and academic coping strategies become critical components of their educational process (Hsieh, Sullivan, Sass, & Guerra, 2012;Luthans et al., 2012;Nicholson, Putwain, Connors, & Hornby-Atkinson, 2013;Sander & Sanders, 2006;Siu, Bakker, & Jiang, 2014). ...
The major purpose of this study was to create a path analysis model of academic success in a group of university students, which included the variables of academic confidence and psychological capital with a mediator variable-academic coping. 400 undergraduates from Marmara University and Istanbul Commerce University who were in sophomore, junior and senior years participated in the study. The Academic Behavioral Confidence Scale, the Academic Coping Strategies Scale and the Psychological Capital Test Battery composed of the Academic Self-Efficacy Scale, the Life Orientation Test, the Hope Scale and the Resilience Scale were utilized to disclose the predictive roles of these variables on academic success. The results of path analyses showed that academic confidence and psychological capital had pivotal direct and indirect effects on academic success via the mediator variable-academic coping. Academic coping had also a direct influence on academic success. The findings of the study are essential for telling both vocational counselors and educational psychologists the fact that career interventions for university students should consider the non-cognitive factors on their academic achievements.
... This implies that the sorting effect will be strong, and the senior high school students in rural and urban cannot be in the same as the innate ability distribution, which makes the comparison more challenging. Fourth, in addition to cognitive abilities, noncognitive abilities (e.g., time management, perceptions of self worth, degree of control that individuals feel they possess over their lives) have been shown to have persistent effects on later schooling and labor market outcomes (e.g., Ransdell, 2001;Heckman, Stixrud, & Urzua, 2006;Lindqvist & Vestman, 2011). Therefore, measuring and understanding the rural-urban divide in students' non-cognitive abilities is another direction to extend the current study. ...
This paper aims to measure and understand the rural-urban student cognitive ability gap in China. Using the China Education Panel Survey (CEPS) 2013/2014 data, we find that the cognitive ability test scores of urban students are approximately 1.41 points (17 percent) higher than those of rural students, on average. This difference is equivalent to 37 and 41 percent of the standard deviation of urban and rural students’ test scores, respectively. Instead of the raw test score, when the cognitive ability is estimated with the 3-parameter Logistic item response theory model, the rural-urban gap is somewhat reduced. The regression and Oaxaca-Blinder decomposition analyses show that nearly one-half of the rural-urban gap can be accounted for by differences in observed characteristics, especially number of siblings, parental education, and interaction between parents and teachers. We then discuss the policy implications of these results and propose a few potential ways to reduce the rural-urban gap in students’ cognitive abilities.
... Relatively stable or immutable individual difference factors are frequently studied as antecedents of academic performance. Of these, the most prevalent are cognitive factors such as intelligence and verbal ability (Chamorro-Premuzic & Furnham, 2008;Dollinger et al., 2008;Furnham, Monsen, & Ahmetoglu, 2009;Ransdell, 2001), and personality factors like conscientiousness (Chamorro-Premuzic & Furnham, 2008;Conard, 2006;McAbee & Oswald, 2013;Poropat, 2009Poropat, , 2014Vedel, 2014). For personality, conscientiousness holds the strongest association with academic outcomes; it should be noted, however, that there is evidence for relationships between multiple aspects of personality (openness, neuroticism, agreeableness, extraversion) and academic performance (e.g., Gallagher, 1996;Laidra, Pullman, & Allik, 2006;Poropat, 2009). ...
The current study used a person-centered approach to explore individual differences in academic performance, as a compliment to traditional variable-centered approaches. Personality traits, intellectual ability, and more mutable study skills, habits, and attitudes were used to predict academic performance as indexed by GPA and variability in grades across classes (academic variability). Conscientiousness, intellectual ability, motivation, and anxiety were identified as the strongest predictors of GPA and academic variability using a variable-centered approach. These factors were included in an exploratory cluster analysis to extract four distinct student profiles: High-Achievers, Low-Achievers, Strugglers, and Settlers. These achievement profiles, and particularly Strugglers and Settlers, express complex within-profile variable interactions that the traditional variable-centered approach failed to capture. Our findings speak to research and practice on academic interventions, and provide fodder for future research on individual differences and performance.
... This approach provides a rich environment and emphasizes an internal emotional management by individual and prevention of problems instead of handling them is essential. Ransdell (2001) In intercultural research assert that in academic progress of students in addition to cognitive factors such as intelligence and aptitude must consider a higher proportion for non-cognitive factors such as socio-economic status of family, brother-sister relationships, parents support in the education of children and pay attention to their emotional issues. Also (Luster & McAdoo, 1994) research to recognize the associated factors with academic progress and compatibility in -African Americans Children. ...
Education is a one of the most important factors in human's growth and development of society. Emotional education as one of human education Types emphasizes on attention to human emotional dimension in education curricula. The purpose of this research is determining the concept of emotional education and risky behaviors and also the study of the educational position in general and curriculum in particular in the prevention risky behaviors between students. This paper is a kind of review essays and its method descriptive - analytical. emphasizes on attention of schools to emotional matters, students family and individual problems, attention to their individual abilities and interests, equal attention to emotional education beside the other human education dimension and emphasis on important role of emotions on students' academic success and particularly in preventing risky behaviors and have a good life and social well-being. on education way, affection and emotion are the most important
and the strongest factor of understanding and relationship that effects on students growth and development, therefore true emotional and attention to students spiritual issues have an important role in prevention of risky behaviors and consequently dynamics and health of the community.
... Academic success positively affects students in a variety of ways: Productivity and success, intellectual skills, personal motivation, the effort on the work, having a prestigious job, and career dynamism are positively related to academic success (Pascarella and Terenzini 2005). Ransdell (2001) discussed the variables affecting academic performance. Examples of these variables were given as verbal and quantitative skills, self-confidence, test-solving skills, willingness to study, family support, and time spent on classroom activities. ...
The research described in this paper aimed to evaluate the extent to which academic performance is affected by student engagement (students’ involvement in school activities and commitment to the school’s mission and rules), academic self-efficacy (the students’ sense of their own capabilities), and academic motivation (the students’ desire to increase their academic performance). The results of the study, which was conducted with the participation of 578 middle and high school students, suggest that cognitive engagement, one of the subdimensions of school engagement, predicts academic performance; however, emotional and behavioral engagement does not predict academic performance. A sense of academic self-efficacy and academic motivation, however, do predict academic performance. Moreover, the sense of self-capability and related motivations of students, as well as the sense of the purpose for their learning are significant variables affecting their academic success.
... Diante do diagnóstico de desempenho abaixo do esperado de grande parcela dos estudantes, verifica-se na literatura que educadores, pedagogos, psicólogos e demais profissionais da educação buscam explicações para as possíveis causas das dificuldades de aprendizagem (Meister e cols., 2001;Peres & Menezes, 2007;Ransdell, 2001;Toleffson & Chen, 1988). Segundo Cunha, Sisto e Machado (2007, p. 147), "as dúvidas e questionamentos são muitos e, não raro, encontram-se nos mitos e preconceitos presentes no cotidiano escolar respostas ditas conclusivas que expliquem o bom e o mau rendimento de um grupo de alunos". ...
Resumo Diante dos resultados de avaliações periódicas nacionais, diferentes aspectos têm sido apontados como influentes no desempenho escolar. Nossa pesquisa pretende mapear fatores psicológicos e relacionais que podem atuar para um melhor ou pior desempenho dos alunos. O objetivo deste trabalho é investigar a interação entre o nível de estresse, a competência percebida, a autoestima geral e o rendimento escolar de um grupo de 48 alunos de 6º ano do Ensino Fundamental. Para medir o nível de estresse, utilizamos a Escala de Stress Infantil de Lipp & Lucarelli e para averiguar a competência perce-bida e a autoestima geral, elaboramos uma tradução da Escala de Competência Percebida de Susan Harter. Os resultados apontam um rendimento escolar significativamente menor em alunos com estresse excessivo e/ou competência percebida baixa nas subescalas cognitiva e social. Palavras-chave: fatores psicológicos; fatores relacionais; rendimento escolar. AssociAtion between psychologicAl And relAtionAl fActors And AcAdemic performAnce in elementAry school abstRact Considering the results of periodic national assessments, different aspects have been identified as influential on school performance. Our research aims to map relational and psychological factors that may affect students' performance. The purpose of this study is to investigate the interaction between the stress level, the perceived competence, the general self-esteem and the school performance of a group composed by 48 students from 6
... Traditionally, the research in this field is focused on cognitive factors, and there is the expectation of increasing the amount of variance on academic success explained by using other factors. Among possible predictors are personality variables, self-concept, and emotional intelligence (Downey et al., 2008;Mayer & Salovey, 1997;Ransdell, 2001). ...
This article analyses the relationship between trait emotional intelligence and academic performance, controlling for the effects of IQ, personality, and self-concept dimensions. A sample of 290 preadolescents (11-12 years old) took part in the study. The instruments used were (a) Trait Emotional Intelligence Questionnaire-Adolescents Short Form (TEIQue-ASF); (b) Children's Personality Questionnaire (CPQ; Form A, Part A); (c) IQ test TIDI-2; (d) Adaptation Questionnaire (CAI-1); and (e) academic performance. A positive and significant correlation coefficient between trait EI measured by the TEIQue-ASF and general academic performance was found. The TEIQue-ASF showed incremental validity to predict general academic performance, after controlling for intelligence, personality, and self-concept characteristics.
... One of the most commonly used predictors of academic success (university grade point average) are standardized college admissions tests. However, they are incomplete predictors of how well a student will do in college coursework; they are necessary but not sufficient markers of progress and prognosis (Ransdell, 2001). Current research indicates that standardized college admissions tests such as the Scholastic Aptitude Test (SAT) and the American College Test (ACT) account for no more than about 20% of the variances in firstyear GPA (Linn, 1989). ...
The present study investigated the effect of Iranian university entrance examinations and the high school grade point average (HSGPA) on agriculture students' achievement. The population included 598 admitted students at the Faculties of Agriculture and Natural Resources, University of Tehran. The specific variables of the study included college grade point average (CGPA) and average of major field specialized courses (FSGPA) as dependent variables. Also, scores in mathematics, physics, biology, and chemistry at the university entrance examinations record (UEX), along with high school grade point average (HSGPA) were the independent variables of the study. The results indicated that high school grade point average (HSGPA) was a significant predictor of agriculture students' academic achievement. With the exception of Food Sciences, there was no significant relationship between the scores in mathematics and students' academic achievement in fields related to agriculture. Keywords: High school grade point average (HSGPA), Average of major field specialized courses (FSGPA), Achievement, University entrance examinations (UEX), College grade point average (CGPA).
... Second, in previous research, individual difference variables have been limited. Variables such as scores on standardized tests and high school grade point average have been used as predictors of postsecondary education retention (Feldman, 1993;Ransdell, 2001). However, these largely discount the affective and personality-based underpinnings of retention in postsecondary education. ...
The current study explored individual differences in ability and motivation factors of retention in first-year college students. We used discrete-time survival mixture analysis to model university retention. Parents' education, gender, American College Test (ACT) scores, conscientiousness, and trait affectivity were explored as predictors of retention. Results indicate gender, ACT scores, and conscientiousness are significant predictors of retention, but parents' education level was not a significant predictor. Positive affectivity and negative affectivity also were significant predictors of university retention when added to the model. Interestingly, once affectivity was added to the model, conscientiousness was no longer a significant predictor, indicating conscientiousness may be an amalgamation of motivation and ability. Implications for research and theory are discussed. (PsycINFO Database Record (c) 2013 APA, all rights reserved)
... The study of academic success in university has generated a sizable literature (see Tinto [1993] for a review). Although much of the early work focused on the impact of previous school performance and/or standardized measures of cognitive abilities, the predictive power of these types of variables was quite limited (Berger and Milem 1999;Randsell 2001). The current study focuses on the influence of self-esteem, others' expectations (peers and teachers) and family support on university students' academic achievement. ...
The current research represents an initial step into the analysis of the effect of self‐esteem, others' (peers and teachers) expectations and family support on academic achievement through learning approaches (deep processing, surface processing and effort). Data were gathered from 553 university students from different faculties of a Spanish university. The analyses, through structural equation modeling, provided support for the positive effects of self‐esteem and family support in university students' learning and achievement. Others' expectations increased both surface learning and effort. Implications for higher education are discussed.
... Following the finding that academic achievement at high school was a poor predictor of academic achievement at university (Mayer and Salovey 1997;Randsell 2001), researchers seeking to predict what factors enable students to make a successful secondary school to university transition have recently turned their attention to emotional intelligence (EI) (Newsome et al. 2000;Barchard 2003;O'Connor and Little 2003;Parker et al. 2004aParker et al. , 2006. Due to concerns about the validity of ability EI, conceptualised as a cognitive ability, the majority of these studies have focused on trait EI, which is a personality trait measured through self-report (e.g., Parker et al. 2004a). ...
The relationship between components of emotional intelligence (EI) (interpersonal ability, intrapersonal ability, adaptability and stress management) and academic performance in English, maths and science was examined in a sample of 86 children (49 males and 37 females) aged 11–12 years during the primary–secondary school transition period. Results indicated that for both males and females, intrapersonal ability had little relationship with academic achievement, while adaptability had the strongest relationship with achievement in all subjects. Gender differences were particularly pronounced for science, for which stronger relationships were observed with all EI components for males. In addition, apparent only for males was a negative relationship between stress management and science. These findings offer support for the current inclusion of a personal and emotional element in the primary school curriculum, and indicate that such training is likely to help males more than females to make a successful transition from primary to secondary school.
... An appropriate theoretical perspective needs to accommodate cultural variations in systems of higher education. Such variations are evident from research across a number of countries (Ardila, 2001;Beekhoven et al., 2003;Finocchietti, 1995;Jallade, 1992;Moortgat, 1996;Niit, 2001;Nurm & Aunola, 2001;Ransdell, 2001;Smith & Naylor, 2001). In Australian, despite the variety of studies, theoretical emphases and cultural variation within different national contexts, little has been added to the general body of theory (Abbott-Chapman, Hughes, & Wyld, 1992;Andrich & Mercer, 1997;Department of Education and Science/Australian Vice-Chancellors' Committee, 1971;Dobson, Sharma, & Haydon, 1997, 1998Lewis, 1994;Linke et al., 1991;Martin, Maclachlan, & Karmel, 2001;McClelland & Kruger, 1993;Power, Robertson, & Baker, 1987;Urban et al., 1999;West, Hore, Bennie, Browne, & Kermond, 1986). ...
This paper is concerned with the pathways students take through their studies at university. A critique of current research demands a fresh approach to explaining student progression, in particular within Australian higher education. To date, theories of student progression commonly consider the fit of the person to the university environment within one rather homogeneous socio-cultural milieu. Socio-ecological approaches provide a new, more appropriate framework for investigating the progression of undergraduate students. Student pathways are conceptualised as a diverse series of choices within the discrete learning contexts of courses. In principle, student pathways and related behavioural outcomes are a function of student characteristics and the supports and constraints within each course. Understanding the differential impact of personal and social characteristics of students and their specific learning contexts contributes to an understanding of the choice behaviour of students as they negotiate common and distinct pathways through courses within the broader context of higher education. This paper presents an appropriate, useful and meaningful theoretical framework for understanding how students navigate the Australian higher education system.
Optimal student performance is integral for successful higher education institutions. The consensus is that big data analytics can be used to identify ways for achieving better student academic performance. This article used support vector machines to predict future student performance in computing and mathematics disciplines based on past scores in computing, mathematics and statistics subjects. Past subjects passed by students were ranked with state of art feature selection techniques in an attempt to identify any connection between good performance in a particular discipline and past subject knowledge. Up to 80% classification accuracy was achieved with support vector machines, demonstrating that this method can be developed to produce recommender or guidance systems for students, however the classification model will still benefit from more training examples. The results from this research reemphasizes the possibility and benefits of using machine learning techniques to improve teaching and learning in higher education institutions.
Enrollment in dual credit classes has increased over the last 10 years benefiting K-20 education and high school students in providing an opportunity to obtain college credits and increasing postsecondary education enrollment. The purpose of this study was to investigate the predictors of achievement through the lens of Social Cognitive Theory by documenting cognitive and noncognitive factors of dual credit students in grades 9–12 at an east Texas university charter school. The Noncognitive Questionnaire was used to measure the noncognitive characteristics, and the Texas Success Initiative Assessment (TSI) reading and writing scores were used as the cognitive measurement. Multiple regression analysis, using weighted least squares, found TSI writing and positive self-concept to be significant predictors of achievement. The findings from this study could suggest states and districts to consider admission requirements for dual credit classes to include other cognitive and/or noncognitive artifacts for admission decisions.
With rapid changes in technology and global competition, the success of many organisations hasbecome progressively more dependent on their ability to bring innovative products to market. Ultimately, however, innovation depends on the generation of novel ideas, which in turn depends on good strategies/techniques. It is in thelight of this that this study investigated the effects of provocation and emotional mastery programmes at fosteringcreative thinking competence of Nigerian Adolescents. The study also aimed at establishing whether gender willmoderate the effects of the two techniques on creativity skills of adolescents. A pretest-post-test 3 x 2 factorial designwas adopted. The simple random sampling procedure was used in selecting participants and assigning them to twotreatments groups and the control group. A total of 270 participants taken among senior secondary school studentsin three randomly selected public secondary schools participated in the study. Data were collected using ideativeoriginality scale (IOS). Analysis of covariance (ANCOVA) was employed for data analysis with significant level fixedat 0.05. Findings from the study revealed that the treatments differentially and significantly affect participants’levels of creativity; Gender, as well as gender and treatment were found not to have any significant effects onparticipants’ levels of creativity. On the basis of the findings, the study advocated for the teaching of creativity asschool subject in secondary schools.
There are many factors influencing a student’s achievement in academic. One of the factors is emotional intelligence (EI). The relationship between EI and academic achievement was investigated in this study. The result showed that one of the EI components (i.e. use of emotion) is positively correlated to EI. The study also revealed that the female respondents scored lower than male respondents in EI mean score. However, the difference in EI between genders is not statistically significant. The difference in EI between years of study was also studied and it was found insignificant statistically. The results revealed an interesting finding that the respondents in this study, regardless of the years of study, self-reported that they are better in appraising their own emotions, but weaker in controlling their emotions.
A decision tree model has been developed to predict student performance in Engineering Dynamics based on 750 data records collected from 125 students in two semesters. The predictor variables include a student's cumulative GPA and scores in four prerequisite courses: Engineering Statics, Calculus I, Calculus II, and Physics. The model generates nine decision rules and shows that a student's performance in Statics and cumulative GPA play the two most significant roles in governing the student's performance in Dynamics. The prediction accuracy of the model is more than 80%, which is at least 14% higher than that of the traditional multivariate regression model.
Prediction of student academic performance helps instructors develop a good understanding of how well or how poorly the students in their classes will perform, so instructors can take proactive measures to improve student learning. Based on a total of 2,151 data points collected from 239 undergraduate students in three semesters, a new set of multivariate linear regression models are developed in the present study to predict student academic performance in Engineering Dynamics - a high-enrollment, high-impact, and core engineering course that almost every mechanical or civil engineering student is required to take. The inputs (predictor/independent variables) of the models include a student's cumulative GPA; grades earned in four prerequisite courses: Engineering Statics, Calculus I, Calculus II, and Physics; as well as scores earned in three Dynamics mid-exams. The output (outcome/dependent variable) of the models is a student's final exam score in the Dynamics course. Multiple criteria are employed to evaluate and validate the predictive models, including R-square, shrinkage, the average prediction accuracy, and the percentage of good predictions. A good prediction is defined as the one with the prediction error of ±10%. The results show that the developed predictive models have the average prediction accuracy of 86.8%-90.7% and generate good predictions of 44.4%-65.6%. The implications of the research findings from the present study are also discussed.
The present study aims to develop a validated set of multivariate regression models to predict student academic performance in Engineering Dynamics-a high-enrollment, high-impact, and core engineering course. The models include eight predictor/independent variables that take into account student achievement before taking the course and student learning progression and achievement while taking the course. A total of 1,674 data points were collected from 186 undergraduate engineering students in two semesters. Four multivariate regression models were generated using different sample sizes of training datasets. The models were evaluated, validated, and compared using multiple criteria including R-square values, shrinkage, and prediction accuracy. The results show that the developed regression models have excellent predictability with 87-91% of the average prediction accuracy, and they have moderate predictability (46-66%) to generate good predictions (a good prediction is defined as a prediction that results in less than 10% of prediction error).
Dentre as múltiplas causas para dificuldades de aprendizagem, pesquisas apontam que aspectos afetivos, psicológicos e sociais exercem forte influência sobre a cognição e sobre a relação que os alunos mantêm com a escola, com os professores e com o saber trabalhado neste espaço. Nesta pesquisa, onde adotamos uma abordagem psicopedagógica, o objetivo foi investigar as percepções de um grupo de professores em relação a atitudes facilitadoras dos processos de ensino-aprendizagem e em relação aos alunos apontados como tendo dificuldades. Os professores foram solicitados a responder um questionário com perguntas abertas e ainda um questionário semiestruturado, adaptado a partir de Toleffson et al. (1990). Os resultados apontam que os professores, em sua maioria, associam as dificuldades dos alunos a processos externos, não percebem ou não salientam dificuldades psicológicas e emocionais de seus alunos. Apesar de a nota ter se mostrado bastante associada a dificuldades de várias ordens, alguns alunos com problemas não foram citados por estarem com notas altas.
The transition from high school to university was used as the context for examining the relationship between emotional intelligence and academic achievement. During the first month of classes 372 first-year full-time students at a small Ontario university completed the short form of the Emotional Quotient Inventory (EQ-i:Short). At the end of the academic year the EQ-i:Short data was matched with the student's academic record. Predicting academic success from emotional intelligence variables produced divergent results depending on how the former variable was operationalized. When EQ-i:Short variables were compared in groups who had achieved very different levels of academic success (highly successful students who achieved a first-year university GPA of 80% or better versus relatively unsuccessful students who received a first-year GPA of 59% or less) academic success was strongly associated with several dimensions of emotional intelligence. Results are discussed in the context of the importance of emotional and social competency during the transition from high school to university.
The relationship between previous academic achievement and subsequent success at university was explored in a retrospective study of 56 UK psychology students. It was found that the subjects studied at A-level, and the grades obtained, did not predict performance at university. In contrast, GCSE grades, in particular those achieved in Science and English, were significant predictors of final year marks. Once at university, first and second year results had an incremental ability to predict final year performance, with an additional effect of undertaking a work placement. The implications of the results are discussed within the context of recent literature relating to cognitive and non-cognitive predictors of academic performance.
The objective of this study was to examine, by gender, whether emotional intelligence (EI), peer social support, and/or family social support partially mediated the influence of verbal IQ on Grade 10 Grade Point Average (GPA) for 192 (96 males, 96 females) students. For males, EI and peer social support predicted GPA and EI mediated the association between verbal IQ and GPA. For females, EI, peer social support, and family support predicted GPA but did not mediate the association between verbal IQ and GPA. This study further examined whether subscales of EI (intrapersonal, interpersonal, adaptability, and stress management abilities), peer social support and family social support (emotional, socializing, practical, financial, and advice) added to the prediction of GPA after verbal IQ, gender, and socio-economic status were controlled. Adaptability, stress management and practical family social support each added to the explanation of variability. None of the peer social support subscales predicted additional variance in GPA.
Reexamines the nature of individual differences in novel and practiced performance on skill learning tasks from an information processing framework. Two major sources of data and discussion are reanalyzed and critically evaluated. One source concerns the changes in interindividual between-subjects variability with task practice; the other pertains to associations between intellectual abilities and task performance during skill acquisition. Early studies yielded mixed results regarding the convergence or divergence of individual differences with practice. Other studies indicated small or trivial correlations between individual differences in intelligence and "gain" scores. More recent studies indicated small correlations between performance measures on skill learning tasks and standard intellectual and cognitive ability measures, as well as increasing amounts of task-specific variance over learning trials. Data confirm the proposition that individuals converge on performance as tasks become less dependent on attentional resources with practice. When appropriate methodological techniques are used and crucial task characteristics are taken into account, intellectual abilities play a substantial part in determining individual differences in skill learning. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Examined individual differences in reading comprehension standards by asking 9 undergraduates enrolled in a "Fundamentals of Psychology" course to describe the criteria they used to decide whether or not they had comprehended textbook chapters. Ss were classified as having a dualistic (fact-oriented) or a relativistic (context-oriented) conception of knowledge on the basis of their ratings of attitude statements drawn from the work of W. G. Perry (1968). Comprehension criteria reported by dualists more often involved the knowledge category found in the B. S. Bloom et al (1956) book,
Taxonomy, and those reported by relativists, the comprehension or application categories. The nature and number of reported comprehension criteria were found to be predictive of course grades. Ss reporting the use of comprehension or application criteria earned better grades than those reporting the use of knowledge criteria; Ss reporting the use of more than one criterion earned better grades than those reporting the use of a single criterion. These data suggest that one's epistemological beliefs may dictate one's choice of comprehension standards and that these epistemological standards, in turn, may control the effectiveness of one's text processing efforts. (22 ref) (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Researchers disagree over whether negative correlates of extensive part-time employment during adolescence are consequences of working or are due to differential selection into the labor force. This study examines the over-time relation between school-year employment and adjustment in a heterogeneous sample of approximately 1,800 high school sophomores and juniors. Analyses indicate both significant selection effects and negative consequences of employment. Before working, adolescents who later work more than 20 hrs per wk are less engaged in school and are granted more autonomy by their parents. However, taking on a job for more than 20 hrs per wk further disengages youngsters from school, increases delinquency and drug use, furthers autonomy from parents, and diminishes self-reliance. Leaving the labor force after working long hours leads to improved school performance but does not reverse the other negatives. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
The Study Management and Academic Results Test (SMART) was developed to measure study- and examination-related cognitions, time management, and study strategies. This questionnaire was used in three prospective studies, together with measures for optimism and test anxiety. In the first two studies, done among 253 first-year students enrolled in four different faculties, the highest significant correlations with academic performance were found for the SMART scales. In a replication study among first-year medical students (n = 156) at a different university, the same pattern of results was observed. A stepwise multiple regression analysis, with academic performance as a dependent variable, showed significant correlations only for the SMART Test Competence and Time Management (Multiple R = .61). Results give specific indications about the profile of successful students.
Since 1964, colleges have been sending data to the College Board Validity Study Service in order to determine the degree to which measures used in admissions predict college performance. These studies have allowed for the monitoring of general trends in the relationship of SAT scores and high school grades with freshman grades. Beginning in the early-1970s and continuing to the mid-1970s, the observed average correlations of SAT scores with freshman grades increased dramatically. In the decade that followed, the strength of this relationship gradually declined. In order to determine to what extent the observed variations in the average correlations might be associated with the composition of colleges conducting validity studies each year, validity data were analyzed for the enrolling classes of 1976 to 1985. Estimates of change, based on a repeated measures estimation approach utilizing data from colleges conducting more than one predictive validity study within the 10-year period, and correcting for the restriction of range in SAT scores and high school record, indicated a general linear trend from 1976 to 1985. The correlations of SAT scores with first-year college grades declined from about .51 to about .47, for a change of approximately .04, There was less change during this period for private colleges, small colleges, and more selective colleges.
To compare the vailidities of the Standard Progressive Matrices, 1938 Series and the Advanced Progressive Matrices for IQ and grade point average (GPA), the Standard Matrices, WAIS, and Minnesota Paper Form Board were administered to 201 college students. The Advanced Matrices, Otis Mental Ability Test (Otis) and Minnesota were given to a second group of 121 students. The validities of the matrices were of low magnitude for GPA and moderate for IQ. No significant differences were found between the validity coefficients of the two matrices tests. The bases for the relationships of the non verbal tests with IQ were postulated as underlying information processing rate and quality.
In this study we investigated the relationship between study time and test scores in a course on learning principles for college education majors. The students were required to keep a continuous log of the amount of time that they spent reading, reviewing, and organizing for the course. Weak relationships with test scores were found for total study time and time spent reviewing. A much stronger relationship was found for time spent organizing the course content. An extreme-groups analysis revealed that students with high test scores spent 40 min per week organizing compared with 10 min per week for students with low test scores. The results support the importance that information-processing theorists attribute to active learning strategies.
A study of the relationships between the Ac, Ai, and Ie scales of the CPI and three criteria-GPA, verbal ability, and quantitative ability-was carried out with six groups of college freshmen. Each group differed from the others in one or both of the following characteristics: 1) sex and 2) enrollment in a psychology-of-adjustment course. The obtained Ac-GPA and Ie-GPA correlations were found to be nonsignificant for all groups used in the current investigation. Ai was significantly related to GPA of the total psychology-of-adjustment and the male psychology-of-adjustment groups. While all three scales were generally unrelated to quantitative ability, Ai and Ie were somewhat related to verbal ability-especially for male freshmen.
Student-generated paragraph summaries require students to pause after reading each paragraph of text in order to write a sentence or less summary in their own words. Paragraph summaries appear to meet the demands of the information-processing theory of learning. This study investigated the effectiveness of reading only, regular note taking, and paragraph summaries for questions at the six levels of Bloom’s taxonomy. Paragraph summaries were most effective at the application and analysis levels and least effective at the synthesis and evaluation levels. These results suggest that paragraph summaries may be most useful in encouraging the essential encoding process.
The purpose of this study was to construct a psychometrically sound inventory that could predict college academic success from students' study habits and attitudes. Success was measured in terms of grade point average. A 37-item inventory was constructed and twice revised by removing ineffective items after being administered to samples of 128 and 56 students. The final 31-item inventory was both reliable and valid. Strong correlations were found between both goal-setting and self-concept and academic success. A curvilinear relationship was found between study time and academic success.
Prediction of performance in an introductory psychology course was examined for 65 1st-yr undergraduates. Ss completed the Comprehension subtest of the Nelson-Denny Reading Test (Form C) and were asked to study a 956-word passage, report their reading strategies, and respond to 18 test questions on the passage. Predictors of performance included Nelson-Denny Comprehension score, reading strategy, study time, retention interval, time to complete the test, and test score. Percentage of total points earned in the course for the semester was the performance criterion. The best set of predictors included Comprehension score, test score, and retention interval. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Predicting college grades of individual students is by no means exact, but overall, there is a moderately good correlation between a student's college grades and his or her previous school record and SAT [Scholastic Aptitude Test] scores.
Such correlations vary in size from college to college, but it is more surprising that correlations averaged across colleges vary from year to year. For example, the average correlation of SAT scores with freshman grade average has decreased in recent years, following an earlier increase. Why is that? Have there been changes in the test? In the grade criterion? Only an integrated series of studies could disentangle the host of factors that might be involved in such correlational trends. Believing that a better understanding of trends in validity coefficients would be useful to colleges, the College Board and ETS [Educational Testing Service] decided to undertake the studies here reported.
This volume comes in two parts. The results of individual research studies are reported in Part II (chapters 4-13).
Chapters 1 through 3 in Part I are less technical and intended for a general professional audience. The purpose of Part I is to furnish useful background information, suggest a framework for thinking about the topic, and summarize what we have found. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
This study investigated the effectiveness of cognitive and psychosocial variables in predicting grade point average (GPA) and retention in 2,600 Caucasian and African American college freshmen in a Southeastern public research university. The authors expected to find more effective prediction models based on gender and race rather than only a model for the overall sample. 4 cognitive (high school GPA; SAT verbal, mathematics, and total scores) and 38 psychosocial and demographic variables were employed in the Non-Cognitive Questionnaire and the First Year Student Survey to predict the students' GPAs and retention. High school GPA was the most significant predictor for first year GPA, but other factors were found to be significantly correlated, including parents' educational level, course load, and extracurricular activities. GPA predictive ability was greatly influenced by race, gender and by factors within genders, including science skills and financial aid. The findings indicated that multivariate models to predict academic performance across gender and race are more effective than a general model for the whole sample. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Thoroughly revised and updated, this is the new edition of the standard handbook on educational measurement.
As it has been in earlier editions—Lindquist (1951) and Thorndike (1971)—this is a comprehensive guide to educational measurement; it brings together in one volume what is known about a wide range of topics, from theory and test construction to testing administration and assessment.
Retaining the scope, authority, and usefulness of previous editions, "Educational Measurement," Third Edition, is an indispensable research tool and reference source for anyone involved in tests and measurement: students, teachers, and scholars, and officials at all levels of education. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Much effort and expense is put into the creation and validation of academic and cognitive measures, yet the research continues to suggest serious limitations in using these tests in educational decision-making. Current research indicates that standardized college admissions tests (e.g., the Scholastic Aptitude Test (SAT), the American College Test (ACT)) predict about 10% to 30% of the variance in first year grade point average (Linn, 1990). Self-regulated learning variables such as motivation and cognitive strategy use (Pintrich, 1989) may enhance our assessment of learning potential. The purpose of this study was to assess the model fit of self-regulated learning variables and ACT in predicting first semester grade point average using structural equation modeling. Although chi-square and goodness of fit indices suggest a poor model fit, the self-regulated learning variables were able to contribute to the explained variance in grade point average above and beyond that of ACT. Future research needs to explore the relationships between self-regulated learning variables and their unique contribution to the prediction of academic performance in college. As more is discovered about self-regulated learning variables, educators can use this information to provide better instructional services to students.
Thesis (Psy. D.)--Indiana University of Pennsylvania. Includes bibliographical references (leaves 75-87).
Thesis (Ph. D.)--University of Southern California, 1996. Includes bibliographical references (leaves 113-132).
Handbook for the individual or group culture free intelligence test, scale 3, Form A
- R B Cattell
- A K S Cattell
Cattell, R. B., & Cattell, A. K. S. (1963). Handbook for the individual or group culture free intelligence test, scale 3, Form A. Champaign, IL: Institute for Personality and Ability Testing.
Educational measurement. The American Council on Education
- R L Linn
Linn, R. L. (1989). Educational measurement. The American Council on Education/Macmillan series on higher education.
The Nelson-Denny reading test
- M S Nelson
- E D Denny
Nelson, M. S., & Denny, E. D. (1993). The Nelson-Denny reading test. Boston: Houghton-Mifflin.
Effect of quality and quantity of study on student grades
- Dickenson
First-year academic success
- Ting
Predicting academic performance
- Feldt
Monitoring text comprehension
- Ryan