Despite the success that instructors and learners often enjoy with online university courses, learners have also reported that they miss face-to-face contact when learning online. The purpose of this inquiry was to identify learners' perceptions of what is missing from online learning and provide recommendations for how we can continue to innovate and improve the online learning experience. The inquiry was qualitative in nature and conducted from a constructivist perspective. Ten learners who had indicated that they missed and/ or would have liked more face-to-face contact following their participation in an online course were interviewed to elicit responses that would provide insights into what it is they miss about face-to-face contact when learning online. Five themes emerged: robustness of online dialogue, spontaneity and improvisation, perceiving and being perceived by the other, getting to know others, and learning to be an online learner. Garrison and colleagues' (Garrison, Anderson, & Archer, 2000) community of inquiry framework was used to interpret the findings.

In many universities there seems to be an "eLearning Contradiction" between the expressed need to integrate technology into the teaching-learning process and what is actually occurring in the majority of classrooms. In this paper we describe the collaborative process we used to design an online Conceptual Framework Learning Object (C-FLO). The object can be viewed at http://innovation.dc-uoit.ca/cloe/lo/cf/ This account is grounded in practical experiences and supported by the research literature. First, we offer a rationale for the development of C-FLO. We then illustrate how an interdisciplinary collaborative perspective enhanced both the process and learning outcomes. The impact of this learning object from both the learners' and professors' perspectives is detailed. Collaborative projects such as C-FLO, where professors share resources and expertise to improve student learning, could be a first step toward addressing the eLearning Contradiction.

Learning analytic implementations are increasingly being included in learning management systems in higher education. We lay out some concerns with the way learning analytics – both data and algorithms – are often presented within an unproblematized Big Data discourse. We describe some potential problems with the often implicit assumptions about learning and learners – and indeed the tendency not to theorize learning explicitly – that underpin such implementations. Finally, we describe an attempt to devise our own analytics, grounded in a sociomaterial conception of learning. We use the data obtained to suggest that the relationships between learning and the digital traces left by participants in online learning are far from trivial, and that any analytics that relies on these as proxies for learning tends towards a behaviorist evaluation of learning processes

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<P>This paper addresses the need for quality e-Learning experiences. We used the Demand-Driven Learning Model (MacDonald, Stodel, Farres, Breithaupt, and Gabriel, 2001) to evaluate an online Masters in Education course. Multiple data collection methods were used to understand the experiences of stakeholders in this case study: the learners, design team, and facilitators. We found that all five dimensions of the model (structure, content, delivery, service, and outcomes) must work in concert to implement a quality e-Learning course. Key themes include evolving learner needs, the search for connection, becoming an able e-participant, valued interactions, social construction of content, integration of delivery partners, and mindful weighing of benefits and trade-offs. By sharing insights into what is needed to design and deliver an e-Learning experience, our findings add to the growing knowledge of online learning. Using this model to evaluate perceptions of quality by key stakeholders has led to insights and recommendations on the Demand Driven Learning Model itself which may be useful for researchers in this area and strengthen the model.</P> <P>Quality has been defined in terms of the design of the e-Learning experience, the contextualized experience of learners, and evidence of learning outcomes (Carr and Carr, 2000; Jung 2000; Salmon, 2000). Quality and design of e-Learning courses, however, are sometimes compromised in an &ldquo; . . . effort to simply get something up and running&rdquo; in response to pressing consumer demands (Dick, 1996, p. 59). Educators and researchers have voiced concern over the lack of rigorous evaluation studies of e-Learning programs (e.g., Arbaugh, 2000; Howell, Saba, Lindsay, and Williams, 2004; Lockyer, Patterson, and Harper, 1999; Robinson, 2001). McGorry (2003) adds, &ldquo;although the number of courses being delivered via the Internet is increasing rapidly, our knowledge of what makes these courses effective learning experiences is limited&rdquo; (p. 160). In an economic environment marked by intensive competition between educational institutions, producing and ensuring quality e-Learning programs will be a competitive advantage to attract learners to post secondary institutions (Daniel, 1996; Duderstadt, 1999).</P> <P>In this study we used a credible model, the Demand-Driven Learning Model (DDLM), (MacDonald, Stodel, Farres, Breithaupt, and Gabriel, 2001) and its companion evaluation tool (MacDonald, Breithaupt, Stodel, Farres, and Gabriel, 2002) to design and evaluate an online course. Several data collection methods were used to understand the experiences of key stakeholders in this case study: learners, design team, and facilitators. In addition to adding to the growing knowledge of online learning, our findings highlight additional elements that could be incorporated into the DDLM to further refine the model.</P>