Danny Liu

From its inception, learning analytics (LA) offered the potential to be a game changer for higher education. However, accounts of its widespread implementation, especially by teachers, within institutions are rare which raises questions about its ability to scale and limits its potential to impact student success. Additionally, amidst the backdrop of higher education's contemporary challenges including massification and diversification, entire cohorts (not just those identified as 'at risk' by traditional LA) feel disconnected and unsupported in their learning journey. Increasing pressures on teachers are also diminishing their ability to provide meaningful support and personal attention to students. For LA, related adoption barriers have been identified including workload pressures, lack of suitable or customizable tools, and unavailability of meaningful data. In this chapter, we present a teacher-friendly ‘LA lifecycle’ that seeks to address these challenges, and critically assess the adoption and impact of a unique solution in the form of an LA platform that is designed to be adaptable by teachers to diverse contexts. In this chapter these contexts span three universities and over 72,000 students and 1,500 teachers. This platform, the Student Relationship Engagement System (SRES), allows teachers to collect, curate, analyse, and act on data of their choosing that aligns to their specific contexts. It also provides the ability to close the loop on support actions and guide reflective practice. In contrast to other platforms that focus on data visualisation or algorithmic predictions, the SRES directly helps teachers to act on data to provide at-scale personalized support for study success. This way, the nuances of learning designs and teaching contexts can be directly applied to data-informed support actions. In our case studies, we highlight how this practical approach to LA directly addressed teachers' and students' needs of timely and personalized support, and how the platform has impacted student and teacher outcomes. Through this, we develop implications for integrating teachers' specific needs into LA, the forms of tools that may yield impact, and perspectives on authentic LA adoption.

Despite the explosion of interest in big data in higher education and the ensuing rush for catch-all predictive algorithms, there has been relatively little focus on the pedagogical and pastoral contexts of learning. The provision of personalized feedback and support to students is often generalized and decontextualized, and examples of systems that enable contextualized support are notably absent from the learning analytics landscape. In this chapter we discuss the design and deployment of the Student Relationship Engagement System (SRES), a learning analytics system that is grounded primarily within the unique contexts of individual courses. The SRES, currently in use by teachers from 19 departments, takes a holistic and more human-centric view of data—one that puts the relationship between teacher and student at the center. Our approach means that teachers’ pedagogical expertise in recognizing meaningful data, identifying subgroups of students for a range of support actions, and designing and deploying these actions, is facilitated by a customizable technology platform. We describe a case study of the application of this human-centric approach to learning analytics, including its impacts on improving student engagement and outcomes, and debate the cultural, pedagogical, and technical aspects of learning analytics implementation.

The progress so far and challenges remaining in developing a functional model of the macromolecular architecture of plasmodesmata are discussed. Details of the macromolecular components identified within plasmodesmata are summarised. Electron tomography and correlative microscopy techniques are explored as potential avenues to overcome the challenges in developing an accurate three-dimensional model of the macromolecular architecture of plasmodesmata. In recent years, some areas of plasmodesmatal biology have been left ignored, largely because the technologies required to advance them have been considered too difficult. For example, there have been no electrophysiological studies of plasmodesmata in the last decade and consequently no advances in our understanding of the rapid regulation of the permeability of plasmodesmata. There has also been no advance on the question of heterogeneity of function of the plasmodesmata within a wall and potential avenues to address this question are considered.

Calnexin (CNX) is a highly conserved endoplasmic reticulum (ER) chaperone protein. Both calnexin and the homologous ER-lumenal protein, calreticulin, bind calcium ions and participate in protein folding. There are two calnexins in Arabidopsis thaliana, CNX1 and CNX2. GUS expression demonstrated that these are expressed in most Arabidopsis tissues throughout development. Calnexin transfer DNA (T-DNA) mutant lines exhibited increased transcript abundances of a number of other ER chaperones, including calreticulins, suggesting a degree of redundancy. CNX1 and CNX2 localised to the ER membrane including that within plasmodesmata, the intercellular channels connecting plant cells. This is comparable with the previous localisations of calreticulin in the ER lumen and at plasmodesmata. However, from green fluorescent protein (GFP) diffusion studies in single and double T-DNA insertion mutant lines, as well as overexpression lines, we found no evidence that CNX1 or CNX2 play a role in intercellular transport through plasmodesmata. In addition, calnexin T-DNA mutant lines showed no change in transcript abundance of a number of plasmodesmata-related proteins. CNX1 and CNX2 do not appear to have a specific localisation or function at plasmodesmata—rather the association of calnexin with the ER is simply maintained as the ER passes through plasmodesmata.

The rise of learning analytics in the last few years has seen fervent development from institutions, researchers, and vendors. However, it seems to have had a laggard reception in higher education. Peering behind some barriers to adoption, we question whether common approaches that address the economics of low hanging fruit distract us from asking and answering deeper questions about student learning. This may lead to destructive feedback loops where learning analytics, swept by the currents of institutional agendas and cultures, does not deliver upon its promises to those who need it most - students and educators.

Plasmodesmata are plasma membrane-lined channels through which cytoplasmic molecules move from cell-to-cell in plants. Most plasmodesmata contain a desmotubule, a central tube of endoplasmic reticulum (ER), that connects the ER of adjacent cells. Here we demonstrate that molecules of up to 10.4 kDa in size can move between the ER lumen of neighbouring leaf trichome or epidermal cells via the desmotubule lumen. Fluorescent molecules of up to 10 kDa, microinjected into the ER of Nicotiana trichome cells, consistently moved into the ER and nuclei of neighbouring trichome cells. This movement occurred more rapidly than movement via the cytoplasmic pathway. A fluorescent 3-kDa dextran microinjected into the ER of a basal trichome cell moved into the ER and nuclei of epidermal cells across a barrier to cytoplasmic movement. We constructed a 10.4-kDa recombinant ER-lumenal reporter protein (LRP) from a fragment of the endogenous ER-lumenal binding protein AtBIP1. Following transient expression of the LRP in the ER of Tradescantia leaf epidermal cells, it often moved into the nuclear envelopes of neighbouring cells. However, green fluorescent protein targeted to the ER lumen (ER-GFP) did not move from cell to cell. We propose that the ER lumen of plant cells is continuous with that of their neighbours, and allows movement of small ER-lumenal molecules between cells.

The fluorescence patterns of proteins tagged with the green fluorescent protein (GFP) and its derivatives are routinely used in conjunction with confocal laser scanning microscopy to identify their sub-cellular localization in plant cells. GFP-tagged proteins localized to plasmodesmata, the intercellular junctions of plants, are often identified by single or paired punctate labelling across the cell wall. The observation of paired puncta, or ‘doublets’, across cell boundaries in tissues that have been transformed through biolistic bombardment is unexpected if there is no intercellular movement of the GFP-tagged protein, since bombardment usually leads to the transformation of single, isolated cells. We expressed a putative plasmodesmal protein tagged with GFP by bombarding Allium porrum epidermal cells and assessed the nature of the doublets observed at the cell boundaries. Doublets were formed when fluorescent spots were abutting a cell boundary and were only observable at certain focal planes. Fluorescence emitted from the half of a doublet lying outside the transformed cells was polarized. Optical simulations performed using finite-difference time-domain computations showed a dramatic distortion of the confocal microscope's point spread function when imaging voxels close to the plant cell wall due to refractive index differences between the wall and the cytosol. Consequently, axially and radially out-of-focus light could be detected. A model of this phenomenon suggests how a doublet may form when imaging only a single real fluorescent body in the vicinity of a plant cell wall using confocal microscopy. We suggest, therefore, that the appearance of doublets across cell boundaries is insufficient evidence for plasmodesmal localization due to the effects of the cell wall on the reflection and scattering of light.

The use of analytics to support learning has been increasing over the last few years. However, there is still a significant disconnect between what algorithms and technology offer and what everyday instructors need to integrate actionable items from these tools into their learning environments. In this paper we present the evolution of the Student Relationship Engagement System, a platform to support instructors to select, collect, and analyze student data. The approach provides instructors the ultimate control over the decision process to deploy various actions. The approach has two objectives: to increase instructor data literacies and competencies, and to provide a low adoption barrier to promote a data-driven pedagogical improvement culture in educational institutions. The system is currently being used in 58 courses and 14 disciplines, and reaches over 20,000 students.

There is a wealth of data already captured by learning management systems, especially from courses that are well-designed to take advantage of a variety of online activities. However, analyses of such data have been largely in aggregated form. This is compounded by database tables that are unwieldy and difficult to interrogate. We present our approach to temporal analytics which combines nascent open standards for the storage and analysis of such data. As a proof of concept, we leveraged the Experience API to transform Moodle data into an informative temporal stream stored in a learning record store, and have designed and developed some representations of learning processes based on the needs of students and staff. These standards and approaches can be adopted by other practitioners and researchers to further the progress of temporal analytics.

To personalise the student experience with large and diverse cohorts, we have introduced a data-informed and flexible approach which tracks and connects students, staff and services. We outline the principles and outcomes of our transition pedagogy and overview the Learning Analytics software we have developed to support it.

Closing the learning analytics cycle necessitates action from data (Clow, 2012; Jones, Beer, & Clark, 2013). Such action often takes the form of ‘interventions’, which are typically communications pushed from staff to students, delivered through email, text message, or other modes. These interventions can have a marked effect on students (Goh, Seet, & Chen, 2012; Beer, Tickner, & Jones, 2014). Learning analytics systems (existing and in-development) that afford this come from Purdue, Apereo, Central Queensland University, Macquarie University, the University of Sydney, and Open Universities Australia, amongst others. Despite the wide and increasing use of student interventions, there has been little discussion on the design considerations, efficacy, and implications of this key learning analytics ‘output’ (Croton, Willis, & Fish, 2014). This panel session brings together practitioners and researchers to discuss key issues around student interventions in learning analytics. Discussion topics will include:
- What affordances do learning analytics systems need to allow for?
- What are the considerations for effective interventions, e.g. composition, mode, timing, tone, content, length, focus, etc.
- How does one decide when to intervene, for whom, and by whom?
- Are there moral obligations to intervene? What happens if we get it wrong?
- What effects do interventions have on student success, self-efficacy, and agency?

Learning analytics is a rapidly growing field of research and practice, investigating complex datasets on learners through the lenses of pedagogy and data science. It is now deeply embedded in a wide range of emerging trends in higher education, from visualising learning to redesigning spaces to catalysing cross-institutional collaboration (Johnson, Adams Becker, Estrada, & Freeman, 2015), and in the Macquarie University Learning and Teaching Green Paper to support curriculum design and student success strategies. Despite widespread interest, learning data is complex, opaque, and difficult to efficiently and efficaciously interrogate.

To address this growing need, multiple concurrent but independent projects (institutionally-supported as well as informal explorations by interested individuals) have appeared at Macquarie. However, these project teams are largely disconnected from each other and, notably, from the general Macquarie audience. As part of a 2015 Innovation and Scholarship Program project on learning analytics, this proposed symposium aims to connect these people and build a coherent community for learning analytics at Macquarie. We are interested in helping people and projects connect to understand and unpack how learning analytics can help to improve student success and improve learning designs. Through this, we hope to address some of the confusion and scepticism around the pedagogical, ethical, and operational concerns related to learning analytics, and importantly consider how it needs to build upon, and not be disconnected from, educational research and models of learning (Gasevic, Dawson, & Siemens, 2015). Participants will be invited to explore these issues in the context of existing and future work in this space, especially with the view of supporting the strategies of the Green Paper.

BACKGROUND AND AIMS

Increasingly, students enter science degree courses with inadequate science backgrounds (Rice, Thomas, O'Toole, & Pannizon, 2009), and poor preparation for deeper investigative learning in tertiary study (Beasley & Pearson, 1999). Intensive bridging courses run prior to first-year courses can help to address these problems (Read, George, Masters, & King, 2004). While bridging course participants exhibited increased confidence levels and assessment performance compared to students with no background (Youl et al., 2005), the impact of such courses should extend beyond building content knowledge, since it is crucial that students are inducted into the university environment and learning culture (Briggs, Clark, & Hall, 2012). Our challenge was to design a bridging course which integrated independent learning, biology conceptual understanding and competencies, as part of an authentic university experience.

DESIGN

An initial comparison of the senior secondary biology syllabus and the first-year biology curricula  identified key concepts students needed to establish (Mitchell & de Jong, 1994). A five-day curriculum was then designed to expose students to active learning of core competencies such as experimental design and laboratory techniques, and the key concepts of cell biology, genetics, evolution, and organismal biology. Workshops and practicals were designed around constructivism and experiental learning (Driver, Asoko, Leach, Scott, & Mortimer, 1994; Kolb & Kolb, 2005) to provide opportunities for guided open inquiry. Complementary lectures were a mixture of interactive and didactic, for exposure to authentic university experiences. Students designed concept maps of bioenergetics and subcellular components using web-based draw.io software, built Prezi summaries to demonstrate multiple scales of genetics, tested evolutionary hypotheses with simulation software NetLogo (Wilensky, 1999), and created slowmation videos (Hoban & Nielsen, 2013) to explain inheritance. They explored the campus to find laboratory samples, and worked through contextualised experiments to solve a family tree mystery, to design an experiment on the effects of salinity, and to visualise their own cells. Small teams collaborated on interactive tasks and scored points for activities ranging from capturing unique images of microorganisms to generating animations of cell division, and this gamified achievement system maintained motivation in the absence of formal assessment (Broussard & Machtmes, 2012). In addition, use of a workbook and the course website, delivered via CourseSites.com, mirrored the university online learning experience.

RESULTS AND EVALUATION

Bridging course students had no senior high school biology experience and at the end of the course self-reported an average of 40% increase in conceptual understanding. They also indicated that course activities were intellectually stimulating (87% agreement), and that teamwork aided their learning (80% agreement). Qualitative comments highlighted consolidation and application of knowledge in workshops and practicals, and increased comfort with university learning styles. Preliminary data from semester 1 biology courses suggest that these students outperformed students with no biology background in three key examinations.

CONCLUSIONS

Our student-centred course applied a range of pedagogical approaches and novel integration of technological tools, to help students build a deep understanding of core biological concepts and competencies while exposing them to authentic university experiences.

Teaching introductory biology classes presents a number of related challenges including low student engagement, large class sizes, and the diversity of student background knowledge and interests. For these reasons, first-year biology units need to instil in students a sense of scientific discovery instead of just delivering content, while providing safe avenues for authentic inquiry-based learning to engage these students. A further challenge for plant scientists is students’ relatively low innate attraction to this subject material compared to the study of humans or furry animals. This presentation outlines a number of new plant-based student-centred practical sessions that were designed and implemented into two introductory biology courses at the University of Sydney. These courses have enrolments of over 600 students each, and introduce a diverse range of biological concepts. By applying various aspects of their biology, plants were integrated into inquiry-based practicals that included digital imaging, enzyme assays, fluorescence microscopy, isolation and analysis of live organelles, preparation of live tissue for histology, and investigation of whole-organism physiology. Although the practicals covered a range of concepts and competencies, they were structured to provide a safe environment for students to build their laboratory and scientific thinking skills which were then applied to the design and analysis of their own open-ended experiments through which they also developed a strong collaborative ethic. Because students were invested in their investigations, they were more engaged with the subject material and, notably, better understood and appreciated the relevance of plants. Operationally, these practicals were designed to be cost-effective, easy to implement, and adaptable, and they addressed the educational principles of scaffolding, engaged enquiry, and research-enriched teaching. Students frequently commented that learning and then independently applying scientific skillsets, as well as collaborating with their peers to investigate an authentic question, produced a much more fruitful and engaging learning experience.

Many large lecture classes are perceived by students to be didactic and impersonal experiences, where lecturing is not synonymous with learning. When compounded with short effective attention spans, a 50-minute one-way information transmission session rarely amounts to a fruitful learning experience for students. One way to address this challenge to engage students is by actively involving them in the lecture experience, and allowing live feedback of student understanding to redirect lectures. This talk provides an overview and a number of practical hands-on usage examples of a modern web-based system that enables efficient student-lecturer interaction, leveraging the ubiquitous personal mobile devices that students use in class. By engaging students this way, we turn potential distractions into powerful learning tools as students become emotionally and intellectually invested in the lecture. These interactions provide rich avenues through which students can be collaboratively engaged in active inquiry as well as learning to apply concepts to contemporary examples. When well-integrated into the curriculum, these interactive technologies can transform large lectures from passive sessions to deeply invested adventures for students and lecturers alike.

We used a matrix of threshold concepts, which incorporates a network of discipline ideas/processes (Ross et al. 2010) and encapsulates our conceptual understanding of biology, as a useful construct for integrating ‘ways of thinking and practicing as a biologist’ into curriculum design and delivery (Taylor, 2006, 2008). Our new laboratory program for first year biology students focuses more explicitly on key abstract threshold concepts such as hypothesis testing, and scale, using challenging hands-on investigations and increased levels of student independence. We also integrated into the course, a broad range of activities which address key problems in understanding and help students to monitor their progress (Lawson, 2000; Oh, 2010; Pederson, 2011). We used our extensive surveys of students’ understanding of concepts such as hypothesis testing, to create a diagnostic survey instrument (Taylor et al 2012, Zimbardi et al 2012), which could be used to track student progress. Similarly our data on student writing of hypotheses and testing protocols (Taylor and Meyer 2010), which showed a huge variation in understanding of the how and what of testing, led us to design a series of experimental design and interpretive writing activities which allowed students to develop and practice their understanding of the concept.

Increasingly teachers are including more and more online content and activities in their unit. Typically teachers do not have the same level of contact with their students in the online compared to the face to face environment. As a consequence, teachers are unable to identify students that may be struggling to complete the unit. Teachers need better methods and tools to give them information about their students so that they can offer timely support to students that are online.

Research has shown that through the use of learning analytics, students at risk of not completing a unit can be identified (Arnold, 2010; Macfadyen & Dawson, 2010). There is some evidence that indicators such as participation in forums, learning management system (LMS) login frequency, and assessment completion have some predictive value for a student's final grade (Macfadyen & Dawson, 2010; Smith, Lange, & Huston, 2012). Purdue University has developed a traffic light interface using student activity in the LMS to successfully increase student completion in their units (Arnold, 2010; Mattingly, 2012). A similar interface called the Moodle Engagement Analytics Plugin (MEAP) exists for Moodle.

As part of a successful Teaching Delivery Grant application for 2014, a team of Macquarie academics and educational professionals are conducting research into this plugin as well as augmenting it to improve its performance and usability. Preliminary research has shown that the MEAP used in specific contexts can be used to identify students that are at risk of not completing the unit (Liu, Froissard, Richards, & Atif, 2015).

This 60 minute workshop aims to give participants an understanding of how simple learning analytics within iLearn can help unit convenors and student support staff identify students who are becoming disengaged in their unit. It will focus on the practical application of a learning analytics tool that could be used to improve unit completion rates.

Audience engagement will be via hands-on experience in working with a learning analytics tool. They will be able to use a course that they are familiar with so that they can apply what they learn to a well understood context. In participating in the workshop they will explore the difficulties in choosing proxies to measure engagement as well as the inherent difficulties in interpreting information.

Immersed in the technology culture growing up, today’s students bring expectations of interactivity, immediacy, and connectedness to their study. A significant challenge for educators is to increase student engagement in this environment. Considering increasingly diverse cohorts, various technology-facilitated practices can be used to more effectively and efficiency engage students with their learning. In this context, there are many papers, websites, and vendors describing the virtues of various technologies that seem to offer attractive alternatives or supplements to traditional teaching approaches.
However, the availability of these technologies poses a number of questions including:

How are these tools being used by real teachers in real situations (both online and face-to-face)?

How do the teachers work within existing budgets, infrastructure and institutional constraints to deliver engaging activities for their students?

Is the effort of learning how to use the technology worth the investment in time?

There are very few opportunities for teachers to display their  innovations and share tips on how to use technology to enhance learning. This workshop will bring together attendees with an interest in using technology with those who are already working in this space to see each others’ work, exchange ideas and build contacts. There will be a focus on innovations with a high ratio of impact to implementation cost.

This presentation will outline two approaches to learning analytics at the University of Sydney and Macquarie University, where staff are closely involved in the coevolution and development of two bespoke learning analytics tools to personalise student-staff interactions at scale.

The University of Sydney system, called the Student Relationship Engagement System (SRES), is a highly-customisable web-based tool that supports the efficient capture and collation of student datasets . A companion mobile app helps staff quickly access and collect student data. Through an embedded Early Warning System, teaching staff can set up fully customisable rules to contact students via personalised emails and text messages. A nascent feature allows staff to explore patterns and relationships within and between datasets.

The Macquarie University system is an enhancement of an existing Moodle plug-in, the Moodle Engagement Analytics Plugin (MEAP). MEAP can readily access data on student assessments, completions, login activity, forum activity, and the gradebook, amongst others, which are customisably represented as ‘risk indicators’. MEAP allows flexible interrogation of these data, and provides staff the ability to send personalised emails to students based on these risk indicators.

At both institutions, these learning analytics approaches have grown from the grassroots to address pressing staff needs, highlighting the importance of this bespoke coevolution process of design, development, and implementation. The systems have enjoyed substantial organic adoption and are associated with positive student outcomes. As open source developments, we are very interested in working together to open up accessible learning analytics to teachers and students.

Learning analytics has promised a lot and delivered little, so far. In this presentation we will give you an overview of a project at Macquarie University that modifies an existing tool to help teachers identify (and importantly) contact students that are “disengaged” and at risk of failing the course. It seeks to make identification and contacting of students an effective and efficient process. Specifically we will present research around the use of LMS indicators that can be used to identify students at risk. We will also share the results of the project to date. Finally we will also demonstrate the modified Moodle Engagement Analytics Plugin together with the concept of a Student Early Alert System.

It is well known that students learn best when they are actively participating in class and collaborating with other students inside and outside of class. A number of new web-based tools are available to promote and encourage this interaction. In class, student response systems can greatly enhance participation and easily turn a didactic monologue into a student-centred experience. Outside of class, students can continue the conversation with each other and instructors in modern, user-friendly, web-based discussion forums. Other online systems allow students to further develop their deep understanding by writing, answering, and providing feedback for topical questions. In this session, I will showcase some free, student- and instructor-friendly online tools (Socrative, Piazza, and PeerWise), outline how they have been applied in large biology and molecular biology classes to enhance the student learning experience, and discuss some hints and tips on applying these systems successfully.