Know thy student! Combining learning analytics and critical reflections to develop a targeted intervention for promoting self-regulated learning.

Abstract

It is well established that a student's capacity to regulate his or her own learning is a key determinant of academic success, suggesting that interventions targeting improvements in self-regulation will have a positive impact on academic performance. However, to evaluate the success of such interventions, the self-regulatory characteristics of students need to be established. This paper examines the self-regulatory characteristics of a cohort of second-year allied health students, using the evaluation of responses to "meta-learning" assessment tasks supported by access data from the learning management system. Students primarily report using learning strategies from the performance and self-reflection phases. Although few reported using forethought strategies, access to preparatory course materials suggests that these were under-reported. Students who reported reviewing lectures as a learning strategy were more likely to access the online lecture recordings; however, higher access was associated with poorer academic performance. Cluster analysis of all available data showed high academic performance was positively associated with early submission of intra-semester assessment tasks but negatively associated with both use of, and reported of use of lecture recordings by students. These findings suggest that early submission of intra-semester assessment may be useful as a predictor of academic achievement.

Use of Learning Analytics Data in Health Care–Related Educational Disciplines: Systematic Review (Preprint)

“…to the learning platforms [13,[18][19][20][21][22][23]. Of these, three studies only recorded connections to the platform or folder and not the specific learning items contained within [18,19,21].…”

“…tests, quizzes), and four studies used URL links to external websites. The majority (14 out of 19) of the studies used more than one type of e-learning resource and six studies used a single type [13,19,24,28,30,31]. Table 3a List of learning analytics data type There were 14 papers which documented learning outcomes (Table 3c) as measured by either an end-of-course exam [22-24, 27, 30, 32, 33] or a combination with course assessments [12,13,19,20,25,31,34].…”

“…[13,20,30,31], and correlation tests (Pearson, Spearman) [26,31,34]. Table 3b List of e-learning content Relating learning analytics data to learning outcomes With regards to the relationship of learning analytics data to objective learning outcomes, 12 of the 13 studies demonstrated significant results [12,13,19,20,23,24,26,27,30,31,33,34], presented below. One study [26] only analyzed students' learning analytics on their self-efficacy responses as a replacement of learning outcome.…”

Mapeamento Sistemático sobre Abordagens de Mensuração de Autorregulação da Aprendizagem

“…Outras duas técnicas que foram utilizadas, foram a classificação e a análise de agrupamento, ambas com dois trabalhos encontrados na literatura. Os trabalhos de classificação (Bondareva, et al, 2013), (Sabourin, Mott, & Lester, 2012) tiveram como foco o comparativo de múltiplos algoritmos de classificação de dados provindos de sistemas de tutores inteligentes e os trabalhos de análise de agrupamento (Lawanto, Santoso, Lawanto, & Goodridge, 2014), (Colthorpe, Zimbardi, Ainscough, & Anderson, 2015) tiveram o objetivo de identificar padrões ou perfis comportamentais de autorregulação.…”

“…Além desses, outros trabalhos utilizaram abordagens baseada em Mineração de Dados Educacionais, nos quais utilizam técnicas como Mineração de Processos (Schoor & Bannert, 2012), (Sonnenberg & Bannert, 2015), Análise de agrupamento (Lawanto, Santoso, Lawanto, & Goodridge, 2014), (Colthorpe, Zimbardi, Ainscough, & Anderson, 2015) e Algoritmos de Classificação (Bondareva, et al, 2013), (Sabourin, Mott, & Lester, 2012 Figura 5: Área de pesquisa declarada na análise dos dados.…”