Credit Recovery and Grade Point Average in an Alternative High School System

Abstract The dropout rates of African American and Hispanic students in the United States are significantly higher than that of White students. Failure to obtain a high school diploma has adverse economic and social implications for these students and for society. The purpose of this study was to assess the relationship between a credit recovery program with key demographic variables and high school GPA, which is a graduation antecedent, for students in an alternative school. Knowles\u27 framework of adult learning theory was used to examine how participation in the credit recovery process in a system of predominantly African American-serving alternative schools predicted GPA while accounting for the influence of student demographic variables. The ex-post facto causal-comparative design involved the analysis of an archival random sample of 168 former students, 84 of whom had taken credit recovery courses and 84 of whom had not. A multiple linear regression model (R =0.257, F(4, 163) = 2.770, p = 0.029) indicated that only gender (β = 0.188, p = .02) significantly predicted the students\u27 GPA, with female students outperforming males. A conclusion is that the implementation of credit recovery programs in U.S. schools does not have any impact on students\u27 GPA. The results suggest weaknesses in program delivery and training and that the review and revision of professional development opportunities for teachers is merited. Drawing from the extant literature, a professional development recommendation was made to improve program effectiveness based on documented best practice examples. Implications for the promotion of positive social change include the evaluation of more robust credit recovery programs capable of improving the graduation rates of U.S. Hispanic and African American students

Measuring self-regulated learning and online learning events to predict student academic performance

The aim of this study is to identify whether the combination of self-reported data that measure self-regulated learning (SRL) and computer-Assisted data that capture student engagement with an online learning environment could be used to predict student academic achievement. Personally engaged study strategies focused on deep-level learning, the process of taking control, and the evaluation of students' own learning characterize SRL. Diverse theories on how students benefit from SRL underline its positive impact on student academic outcomes. Similarly, there is no doubt that the future trend in education leans towards the integration of technolog y into teaching in order to exploit its full potential. To benefit from both approaches, a combination of self-reported data and detailed online learning events obtained from an online learning environment were investigated in relation to their ability to predict student academic achievement. A case study of 54 university students enrolled in a blended-learning course showed that of the tested SRL variables and observed learning activities, student interaction with auxiliary materials that were part of the course helped to predict academic outcomes. Despite the relatively low ability of the model to explain why some students were able to become successful learners, the presented results highlight the importance of analysing online learning events in computer-Assisted teaching and learning. © 2018 Masaryk University, Faculty of Arts. All rights reserved

Improving learning analytics – Combining observational and self-report data on student learning

The field of education technology is embracing a use of learning analytics to improve student experiences of learning. Along with exponential growth in this area is an increasing concern of the interpretability of the analytics from the student experience and what they can tell us about learning. This study offers a way to address some of the concerns of collecting and interpreting learning analytics to improve student learning by combining observational and self-report data. The results present two models for predicting student academic performance which suggest that a combination of both observational and self-report data explains a significantly higher variation in student outcomes. The results offer a way into discussing the quality of interpretations of learning analytics and their usefulness for helping to improve the student experience of learning and also suggest a pathway for future research into this area

ALEKS Constructs as Predictors of High School Mathematics Achievement for Struggling Students

Educators in the United States (U.S.) are increasingly turning to intelligent tutoring systems (ITS) to provide differentiated math instruction to high school students. However, many struggling high school learners do not perform well on these platforms, which reinforces the need for more awareness about effective supports that influence the achievement of learners in these milieus. The purpose of this study was to determine what factors of the Assessment and Learning in Knowledge Spaces (ALEKS), an ITS, are predictive of struggling learners\u27 performance in a blended-learning Algebra 1 course at an inner city technical high school located in the northeastern U.S. The theoretical framework consisted of knowledge base theory, the zone of proximal development, and cognitive learning theory. Three variables (student retention, engagement time, and the ratio of topics mastered to topics practiced) were used to predict the degree of association on the criterion variable (mathematics competencies), as measured by final course progress grades in algebra, and the Preliminary Scholastic Assessment Test (PSATm) math scores. A correlational predictive design was applied to assess the data of a purposive sample of 265 struggling students at the study site; multiple regression analysis was also used to investigate the predictability of these variables. Findings suggest that engagement time and the ratio of mastered to practiced topics were significant predictors of final course progress grades. Nevertheless, these factors were not significant contributors in predicting PSATm score. Retention was identified as the only statistically significant predictor of PSATm score. The results offer educators with additional insights that can facilitate improvements in mathematical content knowledge and promote higher graduation rates for struggling learners in high school mathematics

Factors influencing the success of learning management system (LMS) on students’ academic performance

E-learning systems have gained substantial attention in the educational world.One of them is the Learning Management System (LMS), a pedagogical platform that is based on web technology. The LMS enables instructors to share materials, organize lessons and assessments, and virtually communicate with students to support the learning and teaching process. The aim of this study is to investigate the factors related to LMS that influence students’ academic performance. Quantitative data from 20 respondents at a large Malaysian university are obtained from a 12-item questionnaire. Findings showed that effectiveness of the LMS system and students’ motivation significantly correlated with their academic performance success. The findings suggest that instructors need to pay a greater role in motivating students to use the LMS via innovative and creative means

Effectiveness of Blended Learning in Nursing Education

Currently, teaching in higher education is being heavily developed by learning management systems that record the learning behaviour of both students and teachers. The use of learning management systems that include project-based learning and hypermedia resources increases safer learning, and it is proven to be effective in degrees such as nursing. In this study, we worked with 120 students in the third year of nursing degree. Two types of blended learning were applied (more interaction in learning management systems with hypermedia resources vs. none). Supervised learning techniques were applied: linear regression and k-means clustering. The results indicated that the type of blended learning in use predicted 40.4% of student learning outcomes. It also predicted 71.9% of the effective learning behaviors of students in learning management systems. It therefore appears that blended learning applied in Learning Management System (LMS) with hypermedia resources favors greater achievement of effective learning. Likewise, with this type of Blended Learning (BL) a larger number of students were found to belong to the intermediate cluster, suggesting that this environment strengthens better results in a larger number of students. BL with hypermedia resources and project-based learning increase students´ learning outcomes and interaction in learning management systems. Future research will be aimed at verifying these results in other nursing degree courses.Consejería de Educación de la Junta de Castilla y León (Spain) (Department of Education of the Junta de Castilla y León), Grant number BU032G19, and grants from the University of Burgos for the dissemination and the improvement of teaching innovation experiences of the Vice-Rectorate of Teaching and Research Staff, the Vice-Rectorate for Research and Knowledge Transfer, 2020, at the University of Burgos (Spain)

The relations between self-reported perceptions of learning environment, observational learning strategies, and academic outcome

This study investigated the relations between students’ self-reported perceptions of the blended learning environment, their observed online learning strategies, and their academic learning outcomes. The participants were 310 undergraduates enrolled in an introductory course on computer systems in an Australian metropolitan university. A Likert-scale questionnaire was used to examine students’ perceptions. The digital traces recorded in a bespoke learning management system were used to detect students’ observed online learning strategies. Using the data mining algorithms, including the Hidden Markov Model and an agglomerative hierarchical sequence clustering, four types of online learning strategies were found. The four strategies not only differed in the number of online learning sessions but also showed differences in the proportional distribution with regard to different online learning behaviors. A one-way ANOVA revealed that students adopting different online learning strategies differed significantly on their final course marks. Students who employed intensive theory application strategy achieved the highest whereas those used weak reading and weak theory application scored the lowest. The results of a cross-tabulation showed that the four types of observed online learning strategies were significantly associated with the better and poorer perceptions of the blended learning environment. Specially, amongst students who adopted the intensive theory application strategy, the proportion of students who self-reported better perceptions was significantly higher than those reporting poorer perceptions. In contrast, amongst students using the weak reading and weak theory application strategy, the proportion of students having poorer perceptions was significantly higher than those holding better perceptions