continue to grow as a result of this opportunity, there are increasing concerns regarding low student retention and progression rates for online students in comparison with on-campus students. Reasons for this vary, however, online students report a sense of isolation and disconnection with their studies highlighting the need for educators to utilise effective facilitation to enhance student connections to an online community. In this paper, we investigated facilitation strategies using two case studies. This illustrated how two online instructors used design-based research to evaluate the impact of facilitation strategies on instructor presence, instructor connection, engagement and learning in maths education and human biology subjects. Findings indicate that focusing on social, managerial and technical facilitation strategies resulted in an increased instructor presence and active involvement, which in turn were influential in motivating students to engage with learningonline. The findings have implications for higher education providers and instructors who are tasked with engaging online students. This identifies the importance of targeted onlinefacilitation to enhance learner-instructor and learner-content
Mentoring and Evaluation Evaluation of instructors can be a taboo topic, however, the establishment of an evaluation process illustrates Park’s commitment to academic quality to students, instructors, and the field of distance education. The OIES is unique in that it mentors and evaluates. The mentoring aspect benefits instructors that are new to online instruction or are new to Park University. These new instructors may be unaware of Park specific policies and guidelines, therefore to have them mentored during a term of actual online teaching allows them to function as a bona fide Park online instructor, but still with the reassurance that they can rely on their mentor/evaluator for help and guidance. The evaluation aspect benefits Park Distance Learning (PDL) and the various
courses are included in this section. The idea of intentionally embedding constructivist teaching practices was shared by the participants. Bev’s assignment design was reported as constructivist. She stated that “all these activities they do within that course [are] intentionally constructive.” Cate also described her main teaching philosophy as constructivist. “So it’s just doing that constructivism spiral where everything builds on everything else and kind of tags back to other information. I’m a very constructivist teacher, so I think I just use it naturally.” Deb stated “I think they are given a lot of opportunity to transform those experiences and that, those hands-on experiences and those conversations, transform that into further learning.” Fay described overlapping content and a mindful scope and sequence to the progression of course work. Allowing for revisions on graded work was her way to scaffold learning on an individualized basis. She and Hope were the only instructors who used the cohort model, where preservice teachers go through their degree program as a unified group. Both described the cohort model as an aspect of constructivism.
18 Walberg (1976), in contrast, believed that psychology was a science of mental life and perception was a key aspect of human behaviour. This belief led Walberg (1976) to propose that participants in the learning environments (such as students and lecturers) could quite successfully express their views on various aspects of said environments. The work of Walberg (1976) and Moos (1974) has since led to the development of a variety of valid and reliable learning environment instruments. Each instrument was designed to quantitatively measure different variables within each dimension. The use of these instruments gives an idea of students‘ perceptions of their learning environment. Additionally, the quantitative data can be used to establish associations between numerous other variables, such as achievement and attitudes (Fraser, 1998). Since the development of the Learning Environment Inventory (LEI) in the 1970s, a range of instruments has been developed to understand classroom learning environments. In the last decade, learning environment instruments have been developed specifically for online environments. The OnlineLearning Environment Instrument (OLES) (Trinidad & Pearson, 2004, 2005) was developed to understand more about learners‘ perceptions of their technology-rich learning environment. A modified version of this instrument was used in this study to ascertain students‘ perceptions of their onlinelearning environment at university, details of which are discussed in Section 2.5.
Motorola to enhance quality by reducing errors and defects), the Institute for Healthcare Improvement’s Plan-Do-Study-Act (PDSA) model, continuous quality improvement, and rapid cycle improvement. All of these techniques are intended to lead to manageable and replic- able processes that capture what is learned about work, potentially culminating in the ideal of a learning health- care system  that maximizes quality, safety, service, and affordability, and in many of them, facilitation is a major component. As in other contexts, facilitation assists in defining practice problems and objectives, provides support to teams in achieving objectives, highlights im- portant contextual factors, and assists teams in interpret- ing data and reaching conclusions about action-outcome relationships. In health care, these quality improvement initiatives often engage frontline workers, who have rarely or never been engaged before, in team-based problem- solving. By doing so, facilitation not only also serves to create new or strengthen existing relationships among workers but generates or renews the confidence and com- mitment among frontline team members and frees them to think in a different way about workplace problems, including questioning the underlying values and assump- tions of the processes they are trying to improve. One hallmark of facilitation-based quality improvement initia- tives is encouraging teams to see problems in their work contexts as things that they can affect and modify, rather than “just put up with.” While the focus is on monitoring and evaluation, the aim is on efficiency and perfecting processes, thus teams likely tend to undertake single-loop rather than higher-order learning.
ence between tasks depends primarily on the extent to which the learned movement patterns conflict with those required to per- form the novel task in the same experimental context. Examples include learning to compensate a reversal in the relation between the motion of a control interface and the motion of a visual display ( Lewis et al., 1951 ), learning to compensate a reversal in the direction of an external force field ( Brashers-Krug et al., 1996 ), compensation for opposing visuomotor rotations ( Krakauer et al., 1999 ), and the learning of nonoverlapping se- quences ( Walker et al., 2003 ). In these cases, interference between motor tasks is typically observed as a performance decrement in the learning of the second task. Here, by using a task that had considerable motor redundancy, we showed that learning a prior task not only influenced performance of the second task, but also that the movement solutions used to solve the second task were contingent upon those learned in first task (as indicated by the null space dispersion). The results support the hypothesis that learning rarely occurs on a blank slate ( Zanone and Kelso, 1992 ;
We propose that templates need to be reconsidered and re-categorised based on their purpose. A template for crafting engaging onlinelearning experiences needs to provide a framework for the thinking processes of those involved in the development and building of the course, as well as the thinking to be engaged by the learner. Further investigation of neuroeducational concepts underpinning student learning processes is needed. Refinement of the metacognitive process described by Zull (2011) as: random action → discovery → joy → intentional action → integration → images → symbol → forming memories → predicting → experiential change—is a pattern creating process. The human brain makes sense of life by finding patterns and order (G. Caine, Caine, & Crowell, 1999). It categorizes, finding similarities and differences and comparing and isolating features. In order to conduct this patterning activity, the human brain must have situations to test, to compare, and to resolve. “Learning is required when an entrenched pattern is challenged or disrupted and new answers are needed” (p. 30). Personal reflection based on practical experience is an important step in the metacognitive process, which enables the continuous reorganisation of information within the individual mind. The mysterious process of changing data (experience) into new knowledge is still unexplained (Norden, 2007; Zull, 2011). While we know the brain goes through a series of actions—experiencing, discovering, feeling, reasoning, and decision- making; we do not know precisely how it all comes together. Yet, we do know that the brain requires immersive experiences to build richly integrated neuronal networks. With further research we may be able to find strategies that assist faculty unfamiliar with learning design concepts to more easily move their focus from information packets to learning sequences.
In the machine learning literature, large scale online estimation problems are sometimes addressed with stochastic gradient descent (SGD) algorithms that approximate the computation of a minimum of an empirical risk over a finite dimensional parameter space. These optimization routines operate on a single observation or relatively small “mini-batches” of observations at a time. For example, SGD in classification or prediction problems aim to minimize some (possibly regularized) empirical risk as in logistic regression or a support vector machine (Bottou, 2010) over a finite dimensional parameter (possibly very high dimensional). Though a stochastic gradient descent algorithm takes far more steps to converge than other optimization routines which operate on the full data set at each step, statistically, SGD and some variants can perform well with only a single pass through a data set(Murata, 1998; Xu, 2011). Thus, SGD type methods can be useful as online estimators for finite dimensional parameters that can be expressed as an optimum of an empirical risk. In this manuscript we aim to develop online semiparametric efficient estimators for any pathwise differentiable target parameter in general semiparametric models which are thereby also an estimator for massive data sets by applying them to an ordered partitioning of the data set.
Our findings at Kirkpatrick level 4 showed equivocal results. There was no beneficial change noted in student attitudes to self-directedness. There are a number of possible interpretations of these findings. The lack of change in self-directed readiness may have been due to students having a high level of self-directed learning readiness (score >150) (Fisher et al., 2001) upon entry to medical school, and our findings are in keeping with this. It has also been reported that it may take two years for students to be comfortable with PBL and this may have influenced the lack of change in the pre-and post- scores (DesMarchais, 1993). Examining the SDLRS tool itself, there is some discussion in the literature regarding its sensitivity and appropriateness for this context (Bonham, 1991). Although Guglielmino’s Self-Directed Learning Readiness Scale demonstrated self-directed learning readiness of third year medical students (Shokar et al., 2002), there have been concerns with its use in medical education research and specifically when used to demonstrate the influence of PBL on self-directed learning readiness (Mann et al., 1994; Miflin et al., 1999; Litzinger et al., 2005; Hoban et al., 2005). It is also difficult to draw conclusions regarding the specific impact of PBL on the learning approach of an entire cohort of students; it is possible that a subset of students might demonstrate improvements in readiness for self-directed learning as a result of participation in PBL.
The use of analytics has been prevalent in the business world since the 1990s where it has been justified as a way of making organisational processes more efficient and eliminating wastage. Given the consolidation of neoliberalism in higher education over the last decade (Block, Gray & Holborow, 2012) the application of business intelligence to education is a natural development. In education, the emergence of ‘big data’ (Bienkowski, Feng & Means, 2012) has led to significant interest in the field by a range of stakeholders from administrators to classroom instructors, each hoping to address problems such as student retention, low motivation and lack of engagement. In the educational context it is necessary to distinguish between academic analytics and learning analytics, where the former relates to business intelligence about the organisation (Campbell, Debloi & Oblinger, 2007) and the latter is increasingly concerned with using data to inform pedagogical processes, such as the design of tasks or the nature and scope of instructor-led interventions. Both processes depend heavily on the development of new techniques and algorithms in educational data mining in order to identify synergies and patterns in the data collected (Bienkowski, Feng & Means, 2012).
The promotional activities to date have focused on highlighting the packages as whole pieces, drawing attention to their existence overall in order to encourage staff and students to access online support for their use of the VLE. This work is important and should continue, however students are very strategic in their approach to learning, and are predominantly driven by assessment. The next layer of promoting the tutorials is to work towards embedding parts of the content into the tasks they are given. Evidence from research into how students learn suggests they will make use of those resources that ultimately enable them to achieve their assessment goals (Stover, 2004, 42; Moore & Aspden 2004, 23). If a student is required to work collaboratively using a wiki for example, they may not make the connection back to the Blackboard Tutorial in order to find help with this tool. Embedding a specific piece of tutorial content into their instructions will make direct links between the activity and the support that is available.
“Induction happens with or without a formal program, and it is often an abrupt and lonely process” (Feiman-Nemser, 2001, p. 1030). Formal induction programs have become much more common in recent years. Induction as temporary support is an approach that helps new teachers ease into teaching without being isolated (Feiman- Nemser, 2012, p. 12). Although earlier recommendations for support of new teachers included reduced workloads for both the new teacher and assigned mentor and shifting more challenging student and course loads to more experienced teachers, these supports rarely manifest on the front lines (Feiman-Nemser, 2012, p. 13). The most popular approach for mentoring is an informal buddy system (Feiman-Nemser, 2012; Goldrick et al., 2012). Mentors in this role have no reason to see themselves as more than buddies and are available when called on but do not advise in a sustained manner. Some mentors in this role offer technical advice and emotional support but, again, on an as needed basis. These mentor/buddies are not engaged in ongoing training for the role. This informal approach is often seen as temporary and “when mentoring means little more than occasional check-ins or informal chats, it is not likely to influence instruction, let alone student learning” (Feiman-Nemser, 2012, p. 13).
As the effective use of ICT and technology in course delivery has become more widespread, some researchers have pointed to its impacts on students’ achievement and engagement in the learning process [1, 7]. More recently, McCoog , Henry et al. , and the Bill and Melinda Gates Foundation  highlighted the importance of thoughtful and purposeful use of technology to facilitate students’ achievements. They stated that it should help exploration of other learning avenues in the process of differentiating instruction with clear educational goals. It should also engage students in creative information gap activities and real experiential learning. To address the obstacles to US educational innovations and tap the potential of technology, for instance, the Bill and Melinda Gates Foundation (2010) argued that utilizing technology intelligently can dramatically improve American students’ readiness and completion. Furthermore, the emergence of the Net-generation students, born between 1977 to 1997, has placed additional pressure on universities and their staff to include a prominent role for technology in their teaching and learning. The Net- generations are “demanding a change in the classroom because of their ability to gather information faster than any other generation” .
“synthesize the theories, methods, and findings of both qualitative and quantitative” (Ke, 2009, p. 6) studies related to the design of e-learning and onlinelearning. A qualitative meta-analysis “is an approach towards formulating a complete depiction of the subject” (Ke, 2009, p.6). As part of the analysis, literature was selected based on its relevance to design. Once selected, the studies were numbered, alphabetized and read. Each study was re-read and annotated, focusing specifically on the data, findings, conclusions and implications that related directly to e-learning and online course design considerations (Creswell, 2012). Components were identified from each study and then grouped into common subthemes. Notes were analyzed to identify common themes and findings and topics that occur and reoccur in the studies.
The promise of onlinelearning is twofold: More-effective uses of technology have the potential both to improve student outcomes and to create a more productive educational system. This chapter has worked out the current costs of both vir- tual and blended models—and has articulated where policymakers must ensure there are no barriers to innovation. It has not, however, systematically tackled the question of productivity (i.e., how to improve and maximize student achievement while keeping costs down). The focus on productivity is accompanied by mul- tiple challenges. The first is today’s dearth of high-quality data. Absent broadly accepted measures of student achievement (the “output” side of the productivity equation), calculating productivity is extremely difficult. Emerging policies—such as state and federal accountability statutes outlining universal reporting require- ments around school finances, student achievement, and system performance— have the potential to lead to a greater focus on overall productivity.
These issues motivate the development of adaptive methods, which are no worse than O( p T ) for general convex functions, but also automatically take advantage of easier functions whenever possible. An important step in this direction are the adaptive GD algorithm of Bartlett, Hazan, and Rakhlin  and its proximal improvement by Do, Le, and Foo , which are able to interpolate between strongly convex and general convex functions if they are provided with a data-dependent strong convexity parameter in each round, and significantly outperform the main non-adaptive method (i.e. Pegasos, ) in the experiments of Do et al. Here we consider a significantly richer class of functions, which includes exp-concave functions, strongly convex functions, general convex functions that do not change between rounds (even if they have no curvature), and stochastic functions whose gradients satisfy the so-called Bernstein condition, which is well-known to enable fast rates in offline statistical learning [1, 10, 19]. The latter group can again include functions without curvature, like the unregularized hinge loss. All these cases are covered simultaneously by a new adaptive method we call MetaGrad, for multiple eta gradient algorithm. MetaGrad maintains a covariance matrix of size d ⇥ d where d is the parameter dimension. In the remainder of the paper we call this version full MetaGrad. A reference implementation is available from . We also design and analyze a faster approximation that only maintains the d diagonal elements, called diagonal MetaGrad. Theorem 7 below implies the following:
It is difficult to recognise learning thresholds in the process of online course development and teaching. Some learning thresholds act as ‘gateways or portals’ (Meyer & Land, 2006) to a higher or new level of understanding and, in turn, this leads to the attainment of more difficult and complex learning thresholds. To assist in the recognition of such threshold concepts, Meyer and Land (2005) have highlighted eight key features of learning thresholds that are typically part of the learning process. Learning thresholds are transformative, troublesome, irreversible, integrative, bounded, discursive and reconstitutive, and they typically involve learners entering a state of liminality which is described by Land, Meyer and Baillie (2010) as “a transformative state in the process of learning in which there is a reformulation of the learner’s meaning frame and an accompanying shift in the learner’s ontology or subjectivity” (Land, Rattray & Vivian, 2014, p. 199). Transformation occurs when there is a basic, fundamental and structural change in the perception or view of oneself, the environment or others. Cranton and King (2003) note transformation consequently changes the way one sees things to make meaning of the world. The integrative element of a learning threshold follows in a linear fashion in that it combines the prior knowledge and understanding with a learner’s newly changed perceptions. When learning of this nature is significant, it can be categorised as being transformative.
Changes emerge when actors reconsider their actions through critical thinking and interactions with others. This involves questioning the assumptions that underlie human actions and concepts (Woodhill and Röling 1998). The concept of social learning has been applied frequently to the study of sustainable agriculture (Schneider et al. 2009; Sol et al. 2013) and in this context has been defined as simultaneously transforming the cognitive, social and emotional competences, including attitudes and values related to collective or individual social actors (Rist et al. 2006). During those learning interactions they co-create new meanings, develop their practices and rebuild their identities. Finally, the study of LINSA raises the question whether there is something specific to innovation and learning when related to sustainability. As known, the meaning of sustainability is ambiguous. Sustainability as a concept and practice is interpreted in many different ways, and hence the concept of sustainability needs to be negotiated (Koutsouris 2008; Hermans et al. 2010). Learning and innovation in relation to sustainability means assuming sustainability not as a set of given rules, but as an object – a ‘boundary object’ as we have put it – around which interaction occurs, so that learning and innovation is measured in terms of achievements in understanding the dynamics of coupled social-ecological systems, in setting criteria to evaluate sustainability and improving sustainability performance. Learning and innovation can also be measured as increase in the degree of consensus, and transformation into practice, over the concept of sustainability. As a process of social construction, this alignment may need to take into account not only internal network perspectives, but also broader societal concerns.
OnlineLearning App is an android app that help you to Learn easily. WE have developed an android app because Acc. To survey 50% of teens access internet though mobile and android has 815 market share of mobile market. In this you can learn technologies as well as discuss your doubts with your other mates. The user Registers then login and explore the best video lectures as well as the help of his/her mates. We have used a Server for storing the details of users and also the whole data of chatting between users. This app helps us to fulfil our basic need as teaching- learning is on-going process. It also helps parents by making single search they can find a technology to explore there. At last this app is all about helping today’s students towards the knowledge of different technologies.
Brian Udermann has been in Higher Education for 20 years, has been teaching online for 10 years and has served as the Director of Online Education at the University of Wisconsin-La Crosse since 2007. He writes a monthly column in the publication Distance Education Report and is a sought-after speaker on the topic of online education.