Part I: AN INTRODUCTION TO INQUIRY. 1. Human Inquiry and Science. 2. Paradigms, Theory, and Research. 3. The Ethics and Politics of Social Research. Part II: THE STRUCTURING OF INQUIRY: QUANTITATIVE AND QUALITATIVE. 4. Research Design. 5. Conceptualization, Operationalization, and Measurement. 6. Indexes, Scales, and Typologies. 7. The Logic of Sampling. Part III: MODES OF OBSERVATION: QUANTITATIVE AND QUALITATIVE. 8. Experiments. 9. Survey Research. 10. Qualitative Field Research. 11. Unobtrusive Research. 12. Evaluation Research. Part IV: ANALYSIS OF DATA:QUANTITATIVE AND QUALITATIVE . 13. Qualitative Data Analysis. 14. Quantitative Data Analysis. 15. Reading and Writing Social Research. Appendix A. Using the Library. Appendix B. Random Numbers. Appendix C. Distribution of Chi Square. Appendix D. Normal Curve Areas. Appendix E. Estimated Sampling Error.

社会研究方法基础 = The Basics of Social Research

Learning in Adulthood: A Comprehensive Guide (3rd ed.)

The COVID-19 Pandemic has led to social isolation; however, with the help of technology, education can continue through this tough time. Therefore, this research attempts to explore the Unified The...

https://journals.sagepub.com/doi/pdf/10.1177/0735633120960421

Social Isolation and Acceptance of the Learning Management System (LMS) in the time of COVID-19 Pandemic: An Expansion of the UTAUT Model

Profiling Teachers’ Readiness for Online Teaching and Learning in Higher Education: Who’s Ready?

Blended Learning (BL) creates a ‘rich’ educational environment with multiple technology-enabled communication forms in both face-to-face and online teaching. Students’ characteristics are closely related to the learning effectiveness in the BL environment. Students’ ability to direct themselves in learning and to utilise learning technologies can affect student learning effectiveness. This study examined the impacts of self-directed learning, technology readiness, and learning motivation on the three presences (social, teaching, cognitive) among students undertaking subjects in BL and non-BL (NBL) settings. The results indicated that the BL environment provides good facilitation for students’ social involvement in the class. Student technology readiness plays a stronger role in impacting the teaching presence in a BL environment than NBL environment. These findings imply that a proper BL setting creates a cohesive community and enhances collaborations between students. Prior training of learning technologies can potentially enhance students’ teaching presence.

/pdf/investigating-self-directed-learning-and-technology-1gn4iyhwpn.pdf

Investigating self-directed learning and technology readiness in blending learning environment

Online and blended learning (OBL) is intended for individualising education. However, while OBL attracts a diverse range of students, teachers lack insight into this diversity, which hinders them in anticipating students' individual needs. The present mixed methods' study examines the reasons and values that students in a teacher training programme in higher education attribute to their participation in OBL. Firstly, three motivational profiles were distinguished. Furthermore, the students value the flexibility and the face-to-face moments in OBL. However, based upon students' current experiences, costs - seen as negative aspects of OBL - seem to emerge. While students mainly mention costs regarding education in general (e.g. a high workload), they also indicate specific costs concerning OBL (e.g. harder to organise group work). A cost-value balance affects students' decisions to persist. Therefore, this study provides the values and costs that teachers should bear in mind for each profile.

Students' motivation and subjective task value of participating in online and blended learning environments

In order to fully realise the potential of online and blended learning (OBL), teacher professional development (TPD) strategies on how to teach in an online or blended learning environment are needed. While many studies examine the effects of TPD strategies, fewer studies target the specific important components of these strategies. This study addresses that gap by conducting a systematic review of qualitative data consisting of 15 articles on TPD that targets OBL. Using a meta-aggregative approach, six different synthesised findings were identified and integrated into a visual framework of the key components of TPD for OBL. These synthesised findings are the base for the action recommendations which present specific and contextualised suggestions. Taken together, the findings can inform in-service teachers and trainers, together with further research and development efforts that are concerned with TPD for OBL.

Improving teacher professional development for online and blended learning: a systematic meta-aggregative review

While blended learning in higher education is valued for various reasons such as addressing students' needs for flexibility, blended learning implementation remains a challenging process. Because the teacher lies at the heart of any educational change process, the current qualitative study investigates crucial teacher attributes for blended learning implementation from the perspective of experts. Experts can analyze deep structures of complex organizational problems and hold process knowledge that can generate practical effect. Twelve expert interviews are conducted and reveal two groups of blended learning teacher attributes: seven adaptive attributes such as realizing a pedagogical need for change or creatively connecting technologies to learning processes, and four maladaptive attributes such as a need for a clear understanding of blended learning or feeling anxious about (the implications of) technology. This study utilizes a holistic approach to identify related teacher attributes that critically influence the implementation of blended learning in higher education.

Experts speaking: Crucial teacher attributes for implementing blended learning in higher education

The unified theory of acceptance and use of technology (UTAUT) is a survey instrument used to determine technology usage intention and behaviour. UTAUT consists of four predictor variables; performance expectance, effort expectancy, social influence and facilitating conditions. This study investigates the technology acceptance profiles of university teaching staff, by using the UTAUT predictor variables as clustering variables, in the context of the implementation of a new learning management system (LMS). While students are mostly the focus of research on technology acceptance in higher education, university teaching staff are predominantly overlooked. Using a modified UTAUT questionnaire, 244 university teaching staff from a Belgian university took part in a survey focusing on their acceptance and use of the new LMS. Most studies on LMS acceptance in higher education are variable centred, whereas this research takes a person‐centred approach. This approach will shed new light on how UTAUT can provide information which can be used to interpret the professional development needs during an institution‐wide educational technology implementation. A cluster analysis with the predictor variables of UTAUT as input variables resulted in three distinct groups: a high, moderate and a low scoring cluster. These differences between the clusters were also reflected in the acceptance of the LMS. The results of this study will therefore facilitate decision making and guidelines for the design of institution‐wide professional development initiatives that are targeted towards the needs of specific groups of university teaching staff. Practitioner NotesWhat is already known about this topic Most research has focused on university students, or on general barriers to technology acceptance.The four core predictors of UTAUT are reliable determinants of intention and attitude, which in turn are direct determinants of use.An alternative to measuring actual use, self‐reported intensity of use, has been found to be a direct determinant of actual use.Previous cluster analyses in connection with technology acceptance have shown that there are meaningful differences between groups of users in the way they accept and use a technology.Clustering gives additional information about technology users that may facilitate follow‐up research or support initiatives designed to suit the needs of the groups.What this paper adds A three‐solution cluster analysis reveals three distinct groups of technology acceptance in university teaching staff: High, moderate and low.High users are most likely to innovate.Moderate and low users most likely need additional support as well as increased social influence from policy and decision makers.Implications for practice and/or policy The professional development support needs of each group can be more easily addressed and targeted separately to suit their specific needs.This is an opportunity for policy and decision makers to revise and optimise policies on LMS use as well as on the supportive initiatives needed to facilitate better and more effective use of the LMS.Innovating will require additional support. While the high usage group is clearly more inclined to use the system more innovatively, the two other groups will require additional resources and social pressure to influence their intentions regarding innovating. [ABSTRACT FROM AUTHOR]

Silke Vanslambrouck

Papers

Students' motivation and subjective task value of participating in online and blended learning environments

Improving teacher professional development for online and blended learning: a systematic meta-aggregative review

Experts speaking: Crucial teacher attributes for implementing blended learning in higher education

Clustering University Teaching Staff through UTAUT: Implications for the Acceptance of a New Learning Management System.

A latent profile analysis of adult students' online self-regulation in blended learning environments