<p>This paper presents findings from a study of Australian and New Zealand academics (n = 276) that teach tertiary education students. The study aimed to explore participants’ early experiences of learning analytics in a higher education milieu in which data analytics is gaining increasing prominence. Broadly speaking participants were asked about: (1) Their teaching context, (2) Their current student retention activities, (3) Their involvement in, and aspirations for, learning analytics use, (4) Their relationship with their institution around learning analytics. The sampled teaching staff broadly indicated a high level of interest but limited level of substantive involvement in learning analytics projects and capacity building activities. Overall, the intention is to present a critical set of voices that assist in identifying and understanding key issues and draw connections to the broader work being done in the field.</p>

Several studies have been conducted to evaluate the experience and involvement of academics in learning analytics (LA) due to its potential for improving teaching and learning. However, findings often reflect an educational culture which is indicative of the institutional or national context where the study has occurred, resulting in bias regarding LA perspectives. Therefore, this study seeks to compare and contrast the experiences of LA among academics in Australia and Malaysia, with intentions to learn from each other’s experience. Areas of comparison were: (1) academics’ involvement in LA activities; (2) academics’ responses to the institutional capacity in supporting LA; and 3) academics’ concerns about the ethical issues surrounding LA. A survey of 353 Australian and 224 Malaysian academics revealed similarities and differences. It is evident from these results that the context and infrastructure for LA are at different stages of development in both countries. Nevertheless, the results provide an interesting reflection on academics’ needs, institutional understanding, policies, and educational cultural biases in applying LA in teaching and learning in higher education institutions.

Nutrient monitoring in Micro Indoor Smart Hydroponics (MISH) relies on measuring electrical conductivity or total dissolved solids to determine the amount of nutrients in a hydroponic solution. Neither method can distinguish concentrations of individual nutrients. This study presents the development and testing of a novel spectroscopic sensor system to monitor nitrogen changes in nutrient solutions for MISH systems. The design phase determined that using an inexpensive AS7265x Internet of Thing (IoT) sensor in a transflective spectroscopic application could effectively detect small fluctuations in nitrogen concentraation. Next, a novel transflective sensor apparatus was designed and constructed for use in a MISH system experiment, growing lettuce over 30 days. Two solution tanks of different sizes, 80 L and 40 L, were used in the deployment of the system. Samples from each tank were analyzed for nitrogen concentration in a laboratory, and multilinear regression was used to predict the nitrogen concentrations using the AS7265x 18 spectral channels recorded in the sensor system. Significant results were found for both tanks with an R2 of 0.904 and 0.911 for the 80 and 40 L tanks, respectively. However, while the use of all wavelengths produced an accurate model, none of the individual wavelengths were indicative on their own. These findings indicate that the novel system presented in this study successfully and accurately monitors changes in nitrogen concentrations for MISH systems, using low cost IoT sensors.

Increasingly learning analytics (LA) has begun utilising staff- and student-facing dashboards capturing visualisations to present data to support student success and improve learning and teaching. The use of LA is complex, multifaceted and raises many issues for consideration, including ethical and legal challenges, competing stakeholder views and implementation decisions. It is widely acknowledged that LA development requires input from various stakeholders. This conceptual article explores the LA literature to determine how student perspectives are positioned as dashboards and visualisations are developed. While the sector acknowledges the central role of students, as demonstrated here, much of the literature reflects an academic, teacher-centric or institutional view. This view reflects some of the key ethical concerns related to informed consent and the role of power translating to a somewhat paternalistic approach to students. We suggest that as students are the primary stakeholders – they should be consulted in the development and application of LA. An ethical approach to LA requires that we engage with our students in their learning and the systems and information that support that process rather than assuming we know we know what students want, what their concerns are or how they would like data presented.

Environmental changes and the reduction in arable land have led to food security concerns around the world, particularly in urban settings. Hydroponic soilless growing methods deliver plant nutrients using water, conserving resources and can be constructed nearly anywhere. Hydroponic systems have several complex attributes that need to be managed, and this can be daunting for the layperson. Micro Indoor Smart Hydroponics (MISH) leverage Internet of Things (IoT) technology to manage the complexities of hydroponic techniques, for growing food at home for everyday citizens. Two prohibitive costs in the advancement of MISH systems are power consumption and equipment expense. Reducing cost through harvesting ambient light can potentially reduce power consumption but must be done accurately to sustain sufficient plant yields. Photosynthetic Active Radiation (PAR) meters are commercially used to measure only the light spectrum that plants use, but are expensive. This study presents Adaptalight, a MISH system that harvests ambient light using an inexpensive AS7265x IoT sensor to measure PAR. The system is built on commonly found IoT technology and a well-established architecture for MISH systems. Adpatalight was deployed in a real-world application in the living space of an apartment and experiments were carried out accordingly. A two-phase experiment was conducted over three months, each phase lasting 21 days. Phase one measured the IoT sensor’s capability to accurately measure PAR. Phase two measured the ability of the system to harvest ambient PAR light and produce sufficient yields, using the calibrated IoT sensor from phase one. The results showed that the Adaptalight system was successful in saving a significant amount of power, harvesting ambient PAR light and producing yields with no significant differences from the control. The amount of power savings would be potentially greater in a location with more ambient light. Additionally, the findings show that, when calibrated, the AS7265x sensor is well suited to accurately measure PAR light in MISH systems.