Learning analytics: Prospects and challenges

Owing to its high promises for improving learning support, teaching, and learning outcomes in higher education, learning analytics has captured much interest from both academics and practitioners over the last several years. Considering that it is rooted in several disciplines, researchers and practitioners have approached learning analytics from a range of perspectives. Although many studies concerning learning analytics have highlighted its great potential for improving learning practice, there is little evidence of successful transfer of the suggested potential into the practice of higher education happening. This clearly indicates a need for rethinking many aspects of learning analytics usage: first, the goals that can be achieved, but also the actions necessary to attain these goals. The aim of the descriptive research presented in this paper is to provide an updated and realistic view of the state of the art in learning analytics, its potential benefits, and tangible challenges that need to be overcome for a successful application of learning analytics as educational technology.

Data-driven Modelling of Microfiltration Process with Embedded Static Mixer for Steepwater from Corn Starch Industry

Cross-flow microfiltration using ceramic tubular membrane was applied for treatment of steepwater from corn starch industry. Experiments are conducted according to the faced centered central composite design at three different transmembrane pressures (1, 2 and 3 bar) and cross-flow velocities (100, 150 and 200 L/h) with and without the usage of Kenics static mixer. For examination of the influence of the selected operating conditions at which usage of the static mixer is justified, a response surface methodology and desirability function approach were used. Obtained results showed improvement in the average permeate flux by using Kenics static mixer for 211 % to 269 % depending on experimental conditions when compared to the system without the static mixer. As a result of optimization, the best results considering flux improvement as well as reduction of specific energy consumption were obtained at low transmembrane pressure and lower feed cross-flow rates.