Examination of Factors that Interact to Shape Academic Outcomes in an Animal Nutrition Course

Anecdotes abound in departmental curriculum committees surrounding the purpose and necessity of prerequisite courses for major subjects. Some believe that a student should have as much exposure as possible to subject matter prior to attempting to gain a full understanding of the material. Others believe that prerequisite courses put an undue burden on students to take excessive coursework and that most of the students’ needs can be provided in a single course. The objective of this study was to ascertain the influence of prerequisite courses in animal science, biology, and chemistry on performance of students in an undergraduate animal science course. In the spring semester of 2019, students taking the animal nutrition course at Tarleton State University were enrolled in the experiment. On the second day of class, students were given a comprehensive exam resembling a final examination to assess knowledge on entry to the course. Subsequently, students completed an online survey to record demographics and educational background in relevant courses. Data were analyzed using PROC MIXED and PROC CORR of SAS v. 9.4. Females had an average score of 39 and males had an average score of 35 on the pre-test, but these numbers were statistically similar (P = 0.16). Student scores on the pre-tests were positively correlated the number of biology (r = 0.52; P < 0.01) and chemistry (r = 0.46; P < 0.01) courses taken, but showed no association (r = 0.14; P = 0.37) with the number of previous animal science courses. Similarly, the number of previously taken biology and chemistry courses were positively correlated (r = 0.70; P < 0.01). Results are interpreted to mean that prior experience in biology and chemistry, but not animal science, are major factors for student performance in animal nutrition and should be considered for prerequisite courses.

ABSTRACTStudents’ personal predictors of academic success are particularly relevant for first-year college students, given the specific challenges that these students face when entering higher education (HE). Academic success in HE has been related to multiple factors, including the students’ approaches to learning (SAL), satisfaction (linked to commitment and persistence), study time (effort), and prior academic achievement. This study analyzes the combined effect of these predictors on perceived academic success. Data from 247 students was collected using the Approaches and Study Skills Inventory for Students and other specific measures to assess presage and process variables of academic success. Although academic success is multidimensional and difficult to explain, factors such as prior academic achievement, satisfaction with the course, SAL, and study time contribute to explain perceived academic success in first-year college students.

Assessing Undergraduate Performance on General Education Natural and Mathematical Science Competencies in an Introductory Nutrition Course

Social and emotional development has been considered an important factor in child development which has been placed at the end of the learning spectrum due to high stakes testing. Social and emotional development consists of the relationships an individual has with others, the level of self-control, and the motivation and perseverance a person has during an activity (Bandura, 1989). This study examined the relationship between Hispanic children’s prekindergarten social and emotional development and their academic success. Hispanic children from a large southeastern city whose parents were receiving subsidized child-care were followed from their prekindergarten year through third grade (N=1,978). Several hierarchical regressions were run to determine the relationship between children’s social and emotional development, during their prekindergarten year using the DECA (Devereaux Early Childhood Assessment), and the their academic success, as measured by kindergarten through third grade end of the year reading and mathematics academic grades, second grade SAT (Stanford Achievement Test) scores, and third grade FCAT (Florida Comprehensive Assessment Test) and NRT (Norm Referenced Test) scores. Hierarchical regressions were conducted for each grade and subject in order to control for demographics and prior achievement. The results of this study revealed that for Hispanic children from low-income families, the best predictor for academic success was the children’s prior academic achievement. Social and emotional development showed no significant predictive value for the third grade criterion variables as well as end of the year academic grades in second grade and kindergarten reading. Evidence did suggest that for first grade end of the year academic grades and kindergarten math, social and emotional development had a small predictive value. Further research must be conducted as to why social and emotional development, after controlling for demographics and previous academic achievement, bears such a small predictive value when it is clear that many professionals feel it is the most important factor for school readiness.

This study reported the results of Structural Equation Modelling (SEM) analyses on 13 competing structural models on the inter-relationships among academic achievement and student- and course-related attributes. The samples were Malaysian pre-university students enrolled in two STEM courses (biology, n = 326; mathematics, n = 339; biology only, n = 92; mathematics only, n = 105; biology and mathematics, n = 234). For both courses, interdisciplinary cross-validation was observed for four models which hypothesized that current academic achievement could be predicted (1) directly by prior academic achievement (high school grades) and student approaches to learning (SAL), and (2) directly and/or indirectly by personality, intrinsic motivation and course experience (CE). For at least one course, all constructs (except intrinsic motivation, clear goals and standards, openness and conscientiousness) significantly and directly predicted current academic achievement. The strongest predictor of current academic achievement was prior (high school) academic achievement, with the largest effect sizes, followed by SAL. Current academic achievement was significantly and positively predicted by all CE constructs (except clear goals and standards) for only mathematics, with moderate and large effect sizes. Only one personality construct (neuroticism) significantly and moderately predicted current academic achievement (biology). SAL partially mediated relationships between current academic achievement with workload appropriateness, assessment for understanding and neuroticism for at least one course. Generally, the strongest predictors of SAL were assessment for understanding, workload appropriateness and intrinsic motivation. Multigroup invariance analysis revealed differences in five hypothesized paths, attributable almost entirely to significant paths found in mathematics but not biology (prior [high school] to current academic achievement, conscientiousness to surface approach to learning, intrinsic motivation to deep and surface approaches to learning). Therefore, this study is the first to report course-nuanced differences in the presence of reduced interpersonal differences. The implications of this study is that, besides the importance of prior high academic achievement which might not be within educators’ control, factors in the teaching–learning ecosystem within educators’ control which influence current academic achievement are strongly mediated by SAL, which is itself influenced most by assessment, workload and intrinsic motivation.

Academic success of students has been explained with a variety of individual and socioeconomic factors. Social networks that informally emerge within student communities can have an additional effect on their achievement. However, this effect of social ties is difficult to measure and quantify, because social networks are multidimensional and dynamically evolving within the educational context. We repeatedly surveyed a cohort of 226 engineering undergraduates between their first day at university and a crucial examination at the end of the academic year. We investigate how social networks emerge between previously unacquainted students and how integration in these networks explains academic success. Our study measures multiple important dimensions of social ties between students: their positive interactions, friendships, and studying relations. By using statistical models for dynamic network data, we are able to investigate the processes of social network formation in the cohort. We find that friendship ties informally evolve into studying relationships over the academic year. This process is crucial, as studying together with others, in turn, has a strong impact on students' success at the examination. The results are robust to individual differences in socioeconomic background factors and to various indirect measures of cognitive abilities, such as prior academic achievement and being perceived as smart by other students. The findings underline the importance of understanding social network dynamics in educational settings. They call for the creation of university environments promoting the development of positive relationships in pursuit of academic success.

Empirical research has provided evidence attesting to the potency of two major theoretical frameworks in teaching and learning, namely, achievement goals and effort. The testing of achievement goals and effort in various cross-sectional studies (Dupeyrat & Marine, 2005; Elliot, McGregor, & Gable, 1999; Fenollar, Roman, & Cuestas, 2007) has yielded findings that indicate their positive effects on academic achievement, directly and indirectly via means of other internal cognitive processes. We used latent growth modeling (LGM) procedures to identify and trace the initial states and change in mastery and performance-approach goals, and how they influence academic achievement in mathematics over time. Furthermore, aligning closely to social cognitive theory (Bandura, 1986, 1997), we explore the effects of prior academic achievement and effort on achievement goals and mathematic achievement. This examination involved 234 university students (97 females, 137 males) across six time points: prior academic achievement at Time 1, effort at Time 2, mastery and performance-approach goals at Time 3 to Time 5, and mathematic achievement at Time 6. Existing Likert-scale inventories were used to measure effort, mastery and performance-approach goals. Our LGM analyses indicated a decline in mastery goals and an increase in performance-approach goals over time. Both effort and prior academic achievement influenced the initial states of mastery and performance-approach goals, respectively. Likewise, prior academic achievement and effort contributed to the prediction of mathematic achievement at Time 6.

An educator’s goal is to always explain classroom material to his or her students in the most efficient way possible. Chemistry is a fundamental foundation to the study of animal nutrition, but the concepts of organic chemistry are very difficult to relay to students of animal science, possibly due to the different types of student learners. It could be that mode of presentation may aid in relaying this information to the students. The sample for this experiment included students enrolled in an animal nutrition course at Tarleton State University. Students were presented with five methods of presentation of carbohydrate molecular structure in a laboratory session, each with an increasingly tactile approach: written description, printed 2-dimensional model, printed 3-dimensional model, ball-and-stick model, and self-constructing molecular model kit. Students were then asked to complete a survey to address effectiveness of presentation method. Data were analyzed using SAS v. 9.4. When addressing effectiveness of presentation method, the written description and the printed 2-dimensional model were rated as slightly-to-moderately effective (48.4 and 67.8%, respectively), the printed 3-dimensional model was rated as moderately effective (58.1%), the ball-and-stick model was rated as very effective (48.4%), and the self-constructing model was rated as very-to-extremely effective (80.6%). Cluster analysis revealed that students chose any of the presentation methods over the written description with preference for the self-constructing model. Results are interpreted to mean that presentation of organic chemistry concepts in animal nutrition are more effective when kinesthetic presentation methods are employed.

Online homework has become an important teaching and learning activity due to the impact of COVID-19 pandemic-related restrictions. This study explored the relationship between primary students’ online homework completion and learning achievement. It also investigated the moderating effects of key factors including the role of the students and the involvement of their parents on this relationship. Based on a total of 3,210 Chinese online homework assigned in the Spring of 2020 to fourth grade primary school students in Wuhan, China, hierarchical linear modeling was employed to examine the relationship between primary students’ online homework completion and their learning achievement. Simultaneously, the effects of potential moderators including students’ information literacy, students’ prior academic achievement, parental digital self-efficacy, and parent – teacher partnership were investigated. The results showed a significant positive effect of students’ online homework completion on learning achievement. Moreover, students’ information literacy, students’ prior achievement, and parent – teacher partnership positively moderated this relationship. Based on the findings, practical implications for school administrators, teachers, and parents are discussed herein to promote online homework completion and enhance students’ learning achievement.

The interest of assigning homework is frequently discussed due to its alleged low impact on student achievement. One of the current lines of research is to emphasize the quality of student homework engagement rather than the amount of time spent on homework. The aim of this study was to determine (a) the extent to which students’ prior achievement affects their homework engagement (i.e., time spent, time management, and amount of teacher-assigned homework done), and (b) how students’ intrinsic motivation toward homework may mediate or moderate the relationship between prior achievement and the homework engagement variables. A large sample of students from the first 4 years of Secondary Education (N = 1899) completed questionnaires. The results showed that intrinsic motivation partially mediates, but does not moderate, the effect of prior achievement on the three variables related to homework engagement (time spent, time management, and amount of teacher-assigned homework done). These results highlight the importance of considering both students’ current level of achievement and their motivation toward homework engagement when assigning homework.

The relation between academic achievement and the spontaneous use of design-thinking strategies

Previous research indicates that age, gender, and prior academic achievement have direct effects on students' approaches to learning and their academic performance and progression. Here an investigation is reported which explores the relationships between 60 first-year undergraduate accounting and business economics students' approaches to learning, their age, gender, prior academic achievement, and their subsequent academic performance and progression. Linear regression analyses identified the strongest predictor of first-year academic performance and progression as prior academic achievement (i.e. performance in school examinations). Cluster analysis of the six dimensions of the RASI, academic performance and progression revealed two clusters. The first cluster (labelled ‘effective learner’) had a 75.0% rate of progression; the second (labelled ‘ineffective learner’) had only an 11.7% rate of progression. ‘Effective learner’ scores high on Deep Approach and low on Surface Approach, while the ‘ineffective learner’ scores low on Deep Approach and high on Surface Approach.

Identifying supportive student factors for mindset interventions: A two-model machine learning approach

Background: Animal ownership has been identified as a risk factor for human survivability of natural disasters. Animal guardians have been reported to react or act in ways that may put their own safety and that of emergency services personnel at risk when faced with a natural disaster. Recent research has suggested that this risk factor could be reconfigured as a protective factor, whereby desires to save animals from natural disaster harm could motivate increased planning and preparedness behaviours amongst animal guardians. However, there has been no research to determine if bushfire planning and response behaviours differ between pet owners with low and high attachment; and how the relationship may differ in relation to small or large animals. Methods and procedure: We investigated the relationship between people’s emotional attachment to different types of pets and their preparation and actions during the Pinery bushfire in South Australia in November 2015. Thirty-four people who were impacted by the fire participated in an online survey. Data were collected about their preparedness, planning and response behaviours as well as their animal attachment (high or low). Results: We identified 10 characteristics (behaviours, attributes, skills and beliefs) associated with high animal attachment scores, and eight associated with low animal attachment scores. Discussion: Our discussion of the differences in demographics, preparedness, planning and response characteristics of participants with high and low animal attachment confirms research suggesting that animal guardians take risks to save their animals during disasters. Our findings also support recent propositions that animal attachment and ownership could be used to increase the natural disaster preparedness and survivability of animal guardians. However, making sure that animal attachment functions as a protective factor requires active and effective intervention through education, behaviour change and social marketing strategies. Whilst our study is high in ecological validity, future research with larger samples sizes is required to determine the generalisability of our findings to animal owners and guardians in other locations, facing fires with other characteristics, especially for owners and guardians with low levels of attachment.

PurposeIn recent years, there has been a tremendous increase in the number of applicants seeking placements in undergraduate architecture programs. It is important during the selection phase of admission at universities to identify new intakes who possess the capability to succeed. Admission variable (i.e. prior academic achievement) is one of the most important criteria considered during the selection process. This paper aims to investigates the efficacy of using data mining techniques to predict the academic performance of architecture students based on information contained in prior academic achievement.Design/methodology/approachThe input variables, i.e. prior academic achievement, were extracted from students’ academic records. Logistic regression and support vector machine (SVM) are the data mining techniques adopted in this study. The collected data were divided into two parts. The first part was used for training the model, while the other part was used to evaluate the predictive accuracy of the developed models.FindingsThe results revealed that SVM model outperformed the logistic regression model in terms of accuracy. Taken together, it is evident that prior academic achievement is a good predictor of academic performance of architecture students.Research limitations/implicationsAlthough the factors affecting academic performance of students are numerous, the present study focuses on the effect of prior academic achievement on academic performance of architecture students.Originality/valueThe developed SVM model can be used as a decision-making tool for selecting new intakes into the architecture program at Nigerian universities.