Both Software Engineering and Machine Learning have become recognized disciplines. In this article I analyse the combination of the two: engineering of machine learning applications. I believe the systematic way of working for machine learning applications is at certain points different from traditional (rule-based) software engineering. The question I set out to investigate is “How does software engineering change when we develop machine learning applications”?. This question is not an easy to answer and turns out to be a rather new, with few publications. This article collects what I have found until now.
LINK
The past decades have shown an accelerated development of technology-enhanced or digital education. Although an important and recognized precondition for study success, still little attention has been paid to examining how an affective learning climate can be fostered in online training programs. Besides gaining insight into the dynamics of affective learning itself it is of vital importance to know what predicts trainees’ intention to transfer new knowledge and skills to other contexts. The present study investigated the influence of five affective learner characteristics from the transfer literature (learner readiness, motivation to learn, expected positive outcomes, expected negative outcomes, personal capacity) on trainees’ pre-training transfer intention. Participants were 366 adult students enrolled in an online course in information literacy in a distance learning environment. As information literacy is a generic competence, applicable in various contexts, we developed a novel multicontextual transfer perspective and investigated within one single study the influence of the abovementioned variables on pre-training transfer intention for both the students’ Study and Work contexts. The hypothesized model has been tested using structural equation modeling. The results showed that motivation to learn, expected positive personal outcomes, and learner readiness were the strongest predictors. Results also indicated the benefits of gaining pre-training insight into the specific characteristics of multiple transfer contexts, especially when education in generic competences is involved. Instructional designers might enhance study success by taking affective transfer elements and multicontextuality into account when designing digital education.
MULTIFILE
Contemporary education increasingly involves a blended learning environment, which consists of a combination of offline and online delivery methods. Blended learning environments can motivate students to learn, but designing motivating blended learning environments is challenging and can result in environments that demotivate students. This conceptual article proposes a blended learning design that helps practitioners to design motivating blended learning environments. According to self-determination theory, students are motivated to learn when their three basic psychological needs for autonomy, competence, and relatedness are supported. Competency-based education (CBE) is intended to support students’ basic psychological needs. We have constructed design guidance for CBE programmes that help practitioners to design a combination of offline and online delivery methods that (1) give students choices in time and place to support their need for autonomy, (2) adapt to students’ competency levels to support their need for competence, and (3) stimulate students’ relationship building with peers and teachers to support their need for relatedness. Although the design guidance is tentative, practitioners can experiment with it to design blended learning environments that motivate students to learn.
