Attitude and Motivation for Learning English and their Impact on Performance: A Study on Engineering Students of Jessore University of Science and Technology

Learners\u27 cognitive, metacognitive, individual differences and demographic characteristics have been found having profound impact on their linguistic performance. This study has tried to observe two such factors namely motivation and attitude of the learners and their impact on the learners\u27 proficiency. An adapted version of AMTB and a TEEP test have been used to statistically measure the level of motivation and attitude of the learners for learning English and the correlation between these two learner factors and their language performance. The study has found that learners\u27 overall motivation level is average though instrumental motivation outscores integrative motivation and they have a mixed attitude towards learning English. Neither motivation nor attitude is significantly correlated with learners\u27 proficiency

Correlates of active commuting, transport physical activity, and light rail use in a university setting

Introduction: This study identified correlates of active commute mode, transport physical activity (TPA), and intention to use light rail transit (LRT) at a large university in advance of a new LRT connection to campus. Methods: Staff, faculty and students completed a campus-wide travel survey in 2017. Multivariable logistic and linear regression models assessed associations between individual, organizational and environmental correlates with outcomes of interest in a sample of 6894 respondents to identify factors that may encourage a shift from vehicle to active commute modes and increase TPA. Results: Those who biked or walked to campus exceeded weekly physical activity recommendations in TPA alone. Commuting by transit was associated with higher levels of TPA, compared to vehicle commuting. Greater commute mode enjoyment was associated with active modes. Staff were least likely to commute via active transport (AT) and had fewer minutes of TPA. Women and Asian racial groups were less likely to report TPA. Rideshare and discounted transit pass use were positively associated with all outcomes. Conclusions: New LRT presents a critical opportunity to achieve gains in both campus health and environmental sustainability. The factors identified in this study should be further explored as potential intervention or programmatic targets to encourage mode shift

Validity of Two Awake Wear-Time Classification Algorithms for activPAL in Youth, Adults, and Older Adults

Background: The authors assessed agreement between participant diaries and two automated algorithms applied to activPAL (PAL Technologies Ltd, Glasgow, United Kingdom) data for classifying awake wear time in three age groups. Methods: Study 1 involved 20 youth and 23 adults who, by protocol, removed the activPAL occasionally to create nonwear periods. Study 2 involved 744 older adults who wore the activPAL continuously. Both studies involved multiple assessment days. In-bed, out-of-bed, and nonwear times were recorded in the participant diaries. The CREA (in PAL processing suite) and ProcessingPAL (secondary application) algorithms estimated out-of-bed wear time. Second- and day-level agreement between the algorithms and diary was investigated, as were associations of sedentary variables with self-rated health. Results: The overall accuracy for classifying out-of-bed wear time as compared with the diary was 89.7% (Study 1) to 95% (Study 2) for CREA and 89.4% (Study 1) to 93% (Study 2) for ProcessingPAL. Over 90% of the nonwear time occurring in nonwear periods &gt;165 min was detected by both algorithms, while &lt;11% occurring in periods ≤165 min was detected. For the daily variables, the mean absolute errors for each algorithm were generally within 0–15% of the diary mean. Most Spearman correlations were very large (≥.81). The mean absolute errors and correlations were less favorable for days on which any nonwear time had occurred. The associations between sedentary variables and self-rated health were similar across processing methods. Conclusion: The automated awake wear-time classification algorithms performed similarly to the diary information on days without short (≤2.5–2.75 hr) nonwear periods. Because both diary and algorithm data can have inaccuracies, best practices likely involve integrating diary and algorithm output.</jats:p