Embedding online activities during lecture time: Roll call or enhancement of student participation?

Student attendance has long polarized the higher education sector with reports of no to little effect on student success to positive relationships between attendance frequency at face to face and synchronous online lectures and better student engagement and achievement. This study investigates the impact of embedded online activities during lecture time on student learning by utilizing students’ portable devices to divert undesirable study behaviors such as gaming and social media activity during class. The aim of the learning intervention is to improve attendance at undergraduate engineering lectures as well as providing better connection to the subject content. Study participants were third year Bachelor of Engineering students enrolled in a mandatory “Digital System Design” course as part of their degree at a major research university in New Zealand. To explore the student experience of embedded active learning tasks on engagement and academic achievement, both qualitative and quantitative data were collected from N = 75 students over a three-year period when the course underwent a re-design utilizing a participatory action research approach. Student focus group discussions and learning analytics data revealed that the completion of online activities during lectures can lead to cognitive overload negatively affecting engagement. However, real-time feedback on learning via synchronization of learning tasks with the lecture content improved student–student and student–teacher connections and thereby contributed to a more positive overall learning experience. The role of stimulating learner motivation and attendance is discussed against Keller’s ARCS model and recommendations for teaching practice are given

Data-informed nudges for student engagement and success

Student engagement has never mattered more in college and university education. While the problem of low engagement and underachievement may differ greatly depending on learning contexts their relationship is well-established. Increasingly, digital technologies have allowed teachers to utilize actionable insights gleaned from data about learner engagement and performance to influence students’ choices on regulating their learning behaviour towards academic success. In this context, we apply the framework of nudge theory from behavioural economics to analyse how teachers, as ‘choice architects’, subtly influence students’ decisions. Drawing on four case studies from Australasian universities, we analyse the approach taken by teachers to engage with data and their cohorts to produce personalised nudges that target antecedent factors of student engagement. Since these approaches were enabled by implementing three open-source learning analytics platforms, we also discuss the forms of data (both online and offline) that teachers leveraged, the concrete actions derived from these data, and the positive impacts on student satisfaction, support, and academic success. To assist teachers, designers, and managers implement data-informed nudges in their contexts, we present a practical framework that synthesises student engagement and nudge theory in a data-rich environment, as well as a set of principles that have emerged from our teachers’ experience in nudging students for engagement and success

Data-informed nudges for student engagement and success

Student engagement has never mattered more in college and university education. While the problem of low engagement and underachievement may differ greatly depending on learning contexts their relationship is well-established. Increasingly, digital technologies have allowed teachers to utilize actionable insights gleaned from data about learner engagement and performance to influence students’ choices on regulating their learning behaviour towards academic success. In this context, we apply the framework of nudge theory from behavioural economics to analyse how teachers, as ‘choice architects’, subtly influence students’ decisions. Drawing on four case studies from Australasian universities, we analyse the approach taken by teachers to engage with data and their cohorts to produce personalised nudges that target antecedent factors of student engagement. Since these approaches were enabled by implementing three open-source learning analytics platforms, we also discuss the forms of data (both online and offline) that teachers leveraged, the concrete actions derived from these data, and the positive impacts on student satisfaction, support, and academic success. To assist teachers, designers, and managers implement data-informed nudges in their contexts, we present a practical framework that synthesises student engagement and nudge theory in a data-rich environment, as well as a set of principles that have emerged from our teachers’ experience in nudging students for engagement and success

Learning analytics in the classroom

The field of learning analytics has progressed significantly since the first Learning Analytics and Knowledge (LAK) conference in 2011. In recent years, the emphasis on technical and statistical aspects of data and analytics has given way to a greater emphasis on what these data mean in the classroom context. This panel session is aimed at examining the emerging role that data and analytics play in understanding and supporting student learning in higher education. Specifically, the panel will focus on the importance of transdisciplinarity and how translation from data to action can occur in the classroom context. The aim of this session is to broaden the conversation about learning analytics within the ASCILITE community. From there, the panel will discuss ways in which learning analytics can have a greater impact on learning design in physical and digital learning environments.</p

Learning analytics in the classroom

The field of learning analytics has progressed significantly since the first Learning Analytics and Knowledge (LAK) conference in 2011. In recent years, the emphasis on technical and statistical aspects of data and analytics has given way to a greater emphasis on what these data mean in the classroom context. This panel session is aimed at examining the emerging role that data and analytics play in understanding and supporting student learning in higher education. Specifically, the panel will focus on the importance of transdisciplinarity and how translation from data to action can occur in the classroom context. The aim of this session is to broaden the conversation about learning analytics within the ASCILITE community. From there, the panel will discuss ways in which learning analytics can have a greater impact on learning design in physical and digital learning environments

Identification of suppressors of cytokine signaling (SOCS) proteins in human gestational tissues: Differential regulation is associated with the onset of labor

Inflammatory cytokines secreted by the placenta and fetal membranes are believed to play an important role in the initiation of parturition. The suppressor of cytokine signaling (SOCS) proteins regulate signal transduction by several cytokines that have been reported to affect gestational tissues. The presence, distribution and roles of SOCS proteins, however, have not been described in human gestational tissues. Using reverse transcriptase (RT)-PCR and Western blot analysis we investigated the expression of SOCS1, SOCS2 and SOCS3 mRNA and protein, respectively, by human villous placenta, amnion and choriodecidua (n=3-4). Tissues were obtained from uncomplicated pregnancies at term after either spontaneous labor and vaginal delivery or caesarian section (before labor). Messenger RNAs for SOCS1, SOCS2 and SOCS3 were expressed in all tissue types, irrespective of labor status. SOCS proteins were, however, only detectable in villous placenta and in one case in the choriodecidua. Labor was associated with abrogated expression of SOCS1 and SOCS3 proteins in villous placenta and the choriodecidua sample. Following labor the band for SOCS2 protein increased slightly in size which may indicate post-translational modification of SOCS2. Reduced expression of SOCS proteins in gestational tissues may provide a mechanism by which inflammatory cytokines enter into a positive feedback loop of inflammatory changes leading to delivery