Online Teaching in Social Work Education: Understanding the Challenges

There is a rapidly growing industry of online learning and distance education programs at the Master of Social Work and Bachelor of Social Work levels both within Australia and globally. A number of best practices have emerged from the literature that warrant consideration when delivering social work programs in online learning and distance education modes. Given the significant advancements in technology that are likely to continue into the next decades, social work academic leaders and accrediting bodies must be prepared to address the changing landscape of higher education, including limited financial resources. Social work academics need to become aware and implement best practices in online learning and distance education e-teaching environments to ensure positive student outcomes, student retention, and student engagement to meet the flexibility needs of students in higher education settings. The purpose of this paper is to identify and discuss the challenges and benefits of distance education and online learning for consideration when providing a social work program in these delivery models.

IMPLICATIONS

• Given the significant advancements in technology that are likely to continue into the next decades, social work academic leaders and accrediting bodies must be prepared to address the changing landscape of higher education.

• Social work academics need to become aware and implement best practices in the distance education and online teaching environment to ensure positive student outcomes, student retention, and student engagement to meet the flexibility needs of students in higher education settings.

     

Day versus residential treatment for juvenile offenders: the impact of program evaluation

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The mixed model is defined. The exact posterior distribution for the fixed effect vector is obtained. The exact posterior distribution for the error variance is obtained. The exact posterior mean and variance of a Bayesian estimator for the variances of random effects is also derived. All computations are non-iterative and avoid numerical integrations.