Early Intervention for At-Risk Online Students

then described a study in which students were tested in three categories: online treatment, online control, and face-to-face control. They measured levels of interest using the Course Interest Survey, which is based on the ARCS model. Students in the treatment group received mass emails every two weeks, composed based on the ARCS model to attract attention and convey personal interest in learner success; to remind of student personal control and reinforce confidence and satisfaction by giving students knowledge of what was expected of them. They also included words of support and encouragement, and points of contact for instructor availability. The results from the study showed a lot of improvement from the control and treatment groups, and demonstrated similar results between the face-to-face group and treatment group. (4), 285-298. This article outlines a plan of student online learning retention and intervention for students who are struggling or at-risk. The optional, student initiated support plan, called ‘SOS’, involves five-steps that help students receive scaffolding instruction, learning support and academic coaching to help in times of stress or when problems or issues arise. The steps are to first ask/request to receive the introductory guide and scaffolding, second, answer an SOS planning questionnaire (for helping identify specific issues and problems to be addressed), third, receive academic coaching/scaffolding and individual feedback, fourth, receive an SOS map and mp3 that will help map out success, and fifth, SOS Scaffolding/coaching sessions in the discussion area of the course. The program was optional for students, but the students that went through the program reported great success in engaging and benefitting from community building, individualized education plans, organization skills, study skills, tech skills, and group This article focused primarily on a test done by researchers on the effects of motivational treatments on students and their learning behaviors. The study was mainly geared towards students who were struggling in school because of low optimism or lack of motivation. The study has not been done on students of online learning, but the results found from the focus groups that were tested showed that the motivational treatments helped improve overall class test results, and the students that were part of the goal engagement treatment program outperformed their no-treatment peers by 7-8%. The treatments given were in an online format, and showed significant improvement for students with co-occurring risk factors. 1-19. doi:10.1037/mot0000107 The advantages and disadvantages of using Open Educational Resources in online student learning are analyzed and explained in this article. OER are defined as technology enabled open provision of educational resources for consultation, use, and adaptation by a community of users for non-commercial purposes. (IE lessons, modules, full courses/programs, guides, e-texts, articles, audio tracks, videos, multimedia, or other learning materials) Researchers performed a study at a University level to determine whether using OER as a part of Online Courses significantly helped students improve in their courses. Results showed that even with the limitations of OER, the positive impact it has on students’ learning is significant, especially as far as Interventions and classes were studied, half were randomly assigned to receive interventions of emails sent on behalf of the faculty that included online and in-person resources that could help them improve their course performance. The data collected from the students included “triggers” based on use reports that were significantly related to student final grade. Other interventions that were suggested included supplemental instruction, one-on-one tutoring sessions, or directed study resources. The interventions were shown to significantly improve student final grade. doi: ​ 10.1145/2723576.2723657 This article analyzed the implementation and effects of Attributional Retraining Treatments to help foster engagement and motivation to improve the retention rates of struggling students. A study is presented that focuses on the hypothesis that students’ performances have more to do with psychosocial variables and the nature of learning experiences than academic successes previously experienced. The AR treatments include causal attributions, shifting/changing students perceived control, and motivation treatments. The study concluded that the implementation of these treatments contributed widely to student retention and success as they continued http://jolt.merlot. org/vol8no1/ryabov_0312. htm This article focuses on aiding students in online courses by of collaboration, conversations and interactions the teacher and online support personal. There are five basic approaches to supporting students: one-on-one consultation (clinical model) through an “open-door” policy; separate accredited subjects within existing courses; parallel classes; generic support and teaching and learning partnerships that embed the student learning support into This articulated specific and direct ways that at-risk students were being identified and ways to target interventions toward their specific needs based on various research conducted. Researchers were identified as using a range of computational techniques to predict learning progression (ie Bayesian modeling, cluster analysis, natural language processing, machine learning, predictive modeling and social media analysis). The article stated that while many researchers and institutions are experimenting with small-scale targeted interventions, there is no comprehensive conceptual model, with a strong evidence base that describe how teachers and administrators can use learning analytics to make successful interventions in their own practice. Different techniques such as monitoring VLE data (number of clicks, number of messages posted in a discussion forum, number of computer formative assessments attempted) supplemented by individual learner characteristics (prior educational attainment, socio-economic data, or were shown not to be as effective for identifying at-risk students as other, more in depth analytics, such as researching learners’ activities during continuous assessments over time. The Open University Analytics study used predictive models constructed by machine learning methods from legacy data recorded in the previous presentation of the same module and performance of learners predicted weekly from the predictive models and the learner data of the current presentation to make their predictions. The article went over different methods and protocol to helping students who are at risk as well. Before the start of the course, a learner will be assessed on their strengths and weaknesses in 4 categories, cognitive, social, teaching, and emotional, and specific guidelines are given to support the student throughout the course relevant to the category he or she struggled the most in. (2), 51-56. This article analyzes the pilot residential learning community project at Indiana University Southeast, which targets first-generation students who are academically at-risk. The study tracks the students’ progress and performance to see if they improve in a number of aspects such as student engagement, academic performance, and second-year retention/credit-hour completion. According to the study, research shows that one of the main reasons students are at-risk of dropping out is because they feel like they don’t belong. The RLC works to connect students with each other so that they have a stronger support system that can help in both retention and interventions. To measure the effectiveness of the RLC, surveys were created and given to the students to measure student engagement, data was gathered on retention and credit hour completion, and assessment tools were created to measure in students are sure to succeed. article used collected from different using this redesign method and six characteristics make in article surrounding individual characteristics of students. A study was performed in which students from a large state university were analyzed and the results showed that prior performance in college (cumulative GPA), and class-standing (senior vs non- senior) were significant characteristics related to student retention in online classes. Other factors significantly related include previous withdrawal from online courses, gender, and recipient of academic loans. Demographic variables such as gender, age, and race are potential explanatory variables to the variance in higher education. The article explains and analyzes these factors in relation to the retention of students in online learning courses. 27-48. doi:10.1007/s11162-013-9305-8. This article clearly articulated the uses and structure of different data collection/learning analytics that have been and are being used to help identify at-risk students. According to the article, big data (datasets whose size is beyond the ability of a typical database software tools to capture, store, manage, and analyze) is a lot more productive in the context of helping improve student learning and retentions than other ways of collecting data. Analytics in learning should be cycled through these 5 steps, as proposed by the researchers writing this article: Course level(Learning trails, social network analysis, discourse analysis), Educational data-mining (predictive modeling, clustering, pattern mining) Intelligent curriculum (the development of semantically defined curricular resources), Adaptive content (adaptive sequence of content based on learner behavior, recommender systems) and Adaptive Learning (the adaptive learning process-social interactions, learning activity, learner support, not only content). The article then goes on to describe LMSs and VLEs and their functions and flaws. The article suggests that new ways of using technol