Sapiens’ Guidelines
Sapiens Course. Sapiens' Guideline
In this section you will find guidelines to help you apply the SAPIENS tool. Through 8 video tutorials and a theoretical background based on the experience of the partner universities in the project.
How to define competences: University of Bielfeld.
Introduction
This paper is about a guide for dealing with competencies in the Sapiens tool. This guidance is content related and not about the technical implementation. If you have any questions about the technical implementation, feel free to refer to the manual.
We will first give you our understanding of competencies and then report on two possible approaches.
Understanding of competences of the authors
The current understanding of competence was coined by Weinert:
“In this context, competence is understood as the cognitive abilities and skills available in individuals or that can be learned by them in order to solve certain problems, as well as the motivational, volitional, and social readiness and skills associated with them in order to be able to use the problem solutions successfully and responsibly in variable situations.” (Weinert 2001, pp. 27- 28)
This understanding of a “competence orientation” is still very general. It needs a more precise clarification how such competence orientation can be tailored in the field of education. Basically, one can extract from this orientation (1) a functional understanding of the concept of competence: the mastering of certain requirements is seen as an indicator of competence. This means that on the basis of observable results and expressions in mathematical demanding situations we can conclude on the existence of corresponding mathematical competencies. (2) The orientation testifies to a domain-specific understanding: competencies are related to a limited range of contexts and situations. Thus, based on mastering of corresponding contexts successfully, competencies can be inferred that are necessary for processing these contents. In mathematics, content-related statements can thus be distinguished in different sub-areas or sub-aspects and not only a mathematical competence per se. On the other hand, competencies are not detached from each other, but are an expression of a (3) general understanding. According to this, competencies can be understood as dispositions, i.e., as a manifestation of what is deeply embedded in our cognitive structures and thus goes beyond the description of a single performance, for example, on a specific task alone (see Kleine, 2012).
This understanding of competence can be operationalized using Bloom’s taxonomy. Bloom’s taxonomy consists of hierarchical models for classifying learning goals, of which we consider the cognitive component in SAPIENS:
Now it’s time to explain the practical use of the tool. Watch these video tutorials to find out the main steps to fully understand SAPIENS and how to apply it in your classroom.
Add a new Teacher
Create a new course
Add a new student
New exercise
Showing an exercise
How to register a student in a course
How to answer an exercise
Students' Massive Import
Eventually, let’s talk about the experience applying the SAPIENS tool in a classroom. Let’s see the specific case of a physics course managed by University of Loyola.
Lessons learnt: University of Loyola’s experience
There are many motivations to use software that enables both the design of intelligent feedback and automated evaluation. The Bologna process has been understood as a more serious requirement that instructors be involved in the learning process of their students. The common traditional model where teacher-centered education is followed by a final exam is no longer an option in many cases. This places the responsibility on instructors to create a system that enables information to flow in both directions between them and students. The advent of technology is the opportunity we should seize if we are ever to meet Bologna’s requirements.
A concrete example where we can take advantage of software is in summative assessment through automatic grading. There are many tools for automated grading available for instructors of all levels. Intelligent diagnostic feedback was introduced in the educational community a long time back and it is still a topic of interest for the research community. Some see these tools as the holy grail of independent learning, which changes the role of instructor to that of a guide on the side. This might be true in some areas, as many elaborate online courses have come to show. Then again, a good instructor–student learning interaction is yet to be superseded by any kind of automation.
These instruments are widely used not only in face-to-face environments but also in online scenarios. The reason can be seen in a shift from an information society to a knowledge society. This structural change in society has had important consequences for the educational system. For instance, education and professional preparation have moved from an industrial model to a model that requires continuous learning. Thus, the ways of learning in the knowledge society have been significantly expanded and refocussed. An important example of these new ways of learning is open, flexible, and technology–enhanced learning environments (online education programs, in general, and massive open online courses, in particular). In this regard, the increasing number of students who are enrolled in this type of scenario justifies the development of such tools. Moreover, on the instructor’s side, these electronic systems have facilitated both the creation of personalized course materials and students’ evaluations. Consequently, instructors can dedicate more time to other aspects of the teaching-learning process.
The advantages afforded to assessing and grading automatically are diverse. First, much of what students do online is saved, archived, and stored. This presents huge opportunities for instructors and students in the sense of tracking progress and generating sources of evidence of the student’s learning. Second, the flexible nature of online learning can be leveraged to support diverse approaches to learning. Students have the opportunity to do their work when it suits them.
Motivated by these reasons, we developed SAPIENS as an open and flexible automated grading application for both distance and face-to-face courses. The tool not only has a great flexibility in the creation of content but also provides intelligent feedback. This is particularly important in the context of distance education. Students drift; motivation and discipline are often hard to maintain. To counter that, many tools offer online interaction. Some offer easy creation of personalized material. Some also include warnings for both instructors and students as the course moves on. Fewer have exercises that evolve as answers come in. Even fewer suggest new material to cover areas where the student needs additional support. Some have great flexibility to customize the rules of all the above–mentioned features. Only a few go beyond grading to assessing the causes of better and worse learning processes, looking at competences in context. SAPIENS has all these features and is ready to expand as new templates are proposed.
The traditional fixation on grading has removed many, when approaching technology, from the real potential: not automated grading, but automated evaluation. We now have the in-detail competencies that anticipate and steer the course toward those specific parts with which the class as a whole struggles the most. Individual assessment of skills and competencies is now also possible.
These are some of the lessons learned during the development and implementation of SAPIENS. The first is that we do not have to be too ambitious in the creation of our material. SAPIENS is easy to use in both the creation of learning materials and the daily interaction with students. The first simple achievement that SAPIENS has brought is that, because of the daily feedback and follow-up, students who would typically do none of the exercises of the course (or wait until the last days before the due date) now do all or most of the compulsory ones. The SAPIENS Student’s Reports become the motivation for them not to lag behind, while the Instructor’s Reports provide early warnings on students who need support.
This places students in a completely different frame of mind with regard to the course. First, they are motivated as they see progress and reward every day. Second, they learn enough basics to challenge themselves to tackle exam-level exercises and other more demanding course objectives. Third, they ask for tutoring sessions on a regular basis, making the individual interaction with the instructor a spontaneous process. Finally, although they are aware of their individual responsibility, they share their personalized exercises with their classmates to work together toward the solutions needed. If someone other than the student completes the work, it eventually shows in the abilities the student must show toward the end of the semester.
The conclusions presented here are the testimony of the Physics courses at Universidad Loyola Andalucía for the duration of SAPIENS as a project, where we showed a way to structure a course to increase students’ engagement. It is the customization, the automation, and the provision of daily feedback that create this atmosphere and this degree of involvement from both instructors and students. This is an emergent behavior not foreseen merely by looking at the syllabus of the subject.