This paper presents an innovative API to help a trainee-teacher design Formative-Assessment (hereafter FA) Freirean-dialogues in a data-analytics education project. The API is emerged in context of our act to help a trainee-teacher in Germany understand what hinder the Syrian oppressed students/immigrants express freely their thoughts/critiques in group-discussions about how to initialize data-analytics education project for their self-overcoming goals. The paper shows how we interpret Freire's two works “pedagogy of the oppressed [1] and pedagogy of freedom [2]” and expose our interpretations as API's design-elements and use-value guidelines for trainee teacher's two helping functions; the first is to help him/her coordinate critical reading/analyzing circles for practicing Freirean multi-factor analysis of oppression in context of the Syrian adult oppressed learners. The second is to help him/her design and prepare FA Freirean dialogues to assess formatively (i.e., analyze critically) a limit and oppressive situation of Syrian adult learners. Moreover, it enables him/her to prepare advanced FA Freirean dialogues for analyzing critically the limit and oppressive situation and its Freirean untested feasibilities for self-overcoming. To test the API's two helping functions, test cases are suggested as next steps of this work-in-progress paper. Finally, for reflective-feedback exchange with FIE community we summarize the API's limits and our next steps to improve it.

This Research to Practice Full Paper is driven by the question: In workshops to develop deep-learning API-features, how can trainer be effectively supported in specifying and formative assessing of individual tasks (i.e. the API-features) for each student? In formative assessment, the trainer’s main difficulty in specifying the tasks is to ensure that the challenges in a task are calibrated to the particular needs of a student at a particular time (i.e. the student’s available skills). While goals that are very high challenging lead to a student’s anxiety, goals that are too easy generate student’s disengagement and boredom. This paper presents a specification tool designed to support the trainers in practicing Hattie’s Visible Learning [1] pedagogy in dialogues for tasks specification and formative assessments. Its design enables two support functions in visualization; 1) a function to declare visually a task specification to make its mastery goals clear and progressively challenging, and 2) a function to refactor a task specification for visualizing formative assessment feedback in dialogues for reflection. Besides the two functions, its innovative design enables the flow model based analysis to understand student engagement. The analysis is according to Csikszentmihalyi’s flow model to diagnose, in dialogues for formative assessments, one of student's eight emotional states (i.e. boredom, apathy, worry, anxiety, control, arousal, and flow) in terms of challenge level and skill level. To verify the tool effectiveness in trainer-student Freirean dialogues, we conducted our experimental tests in workshops where students with different culture backgrounds, and different levels of prior skills have tasks to develop API-features based on deep-learning principles and algorithms. The algorithms selected in the test cases are for automatic classification of images or of text documents. The test results show the effective use of the tool to create conditions to overcome student’s negative emotions (e.g. worry or anxiety), and help him/her experience positive emotions (e.g. control or flow) in learning and mastering the algorithms; to put it differently, to create situations in initial specification and co-specification of tasks, where student’s skills are engaged by progressively higher challenges to understand the deep-learning based classifier algorithms.

This Research to Practice Full Paper is driven by the question: Within limited time resources available to trainers in projects for Big Data Analytics (BDA) problems, how can they define project requirements for Formative Assessment (FA) actions? The paper suggests BDA APIs architecture as helping tool for formative assessors. It helps them effectively produce and adapt visual diagnostic reports for FA-actions in agile based requirements (i.e. features) definition. The paper presents two core architectures: Architecture for a parametrized feature-descriptor-system to define/refine a BDA API feature and its visual diagnostic reports, and an initial resources architecture for BDA API to initialize an analytics algorithm with its input big data sets. Clarifying visually the trainee's challenges (i.e. incremental features in a BDA API) is our main FA action. The FA action is designed based on Csikszentmihalyi's flow model to support a trainee in matching balance between his/her challenges and his/her skills. To test the architecture's functions, the paper has test setups for two formal projects (each has 1 to 6 trainees) and two informal projects (each has 1 to 3 trainees). The projects are to attack BDA problems in learning analytics and in image automatic classification. The test results show that the visual diagnostic reports produced by the trainers are very effective in clarifying visually incremental BDA API features not only for simple classifiers (i.e. classical data mining algorithms) but also for complex classifiers (i.e. deep learning algorithms). The results show also how visual diagnostic reports are easily produced for comparing the algorithm performances using different input big data sets, whereas other reports are produced for comparing performances between different algorithms, using one input data set. Related works are also discussed to show the architecture's differences and advantages. Its main advantages are: 1) it enables the trainers to use deep learning algorithms beside classical data mining algorithms in its BDA API parameterizable feature descriptors for visual diagnostic reports. 2) The descriptors can be extended, reused, shared, and scaled out to help trainers in other universities providing flow model based FA actions. 3) Finally, it has extensions to integrate other theoretical frameworks like Buckingham Shum and Deakin Crick's framework for dispositional learning analytics instead of the used flow model.

This Research to Practice Full Paper is driven by the question: How can a teacher of a large class be effectively supported for formative assessment? The paper suggests a framework for automatic classifiers / predictive models and its data integration tool for teachers. To design an input data set for automatic classifier, the tool enables the teachers to integrate data extracted from paper-based exams; computer assisted formative assessments and LMSs, with learning disposition data, collected by applying the Buckingham Shum and Deakin Crick's theoretical framework for dispositional learning analytics. The suggested framework and its tool are tested with the real assessment data of 129 students collected during conducting the Computer Organization (hereafter CO) course. The results of this CO test scenario show how teachers can interpret the outcomes of the automatic classifiers as decision-support recommendations to improve the planning for formative assessment. As an example, it is presented and discussed how the CO teacher can improve his/her strategies in formative assessment for different students groups (at-risk students, medium students, good students and excellent students). The paper shows also how reports for classifiers accuracy comparison can be produced and understood by teachers. Related works are discussed to show the differences and benefits of the presented framework. Main advantages are: the possibility to use its automatic classification algorithms instead of the statistical regression algorithms and the possibility to use its integration tool to integrate data collected from applying a theoretical framework with data from e-learning /e-assessment as input data sets for automatic classifiers.

The ROBSY approach is able to autonomously generate highly flexible FPGA-based test instruments for structural testing of printed circuit boards (PCBs). This paper deals with the development of an essential part of ROBSY: the test-processor. It is specialized for board-level testing and includes configuration parameters for the adaptation to the specific test case. The level of adaptation is essential for ROBSY and is not presented in other test-processors found in the literature. In order to evaluate the viability of ROBSY, the ROBSY test system was used to test the interconnections to an SRAM device. The experiments demonstrate that the ROBSY approach and test-processor are suitable for board-level testing.

Learning Analytics (LA) has emerged as a significant area of research in the field of technology-enhanced learning. It automatically analyzes educational data in order to enhance students learning experience and to foster their learning. Open learning analytics (OLA) extends this field in that it integrates data from distributed and heterogeneous sources, serves different stakeholders with very diverse interests and needs, and leverages a variety of statistical, visual and computational tools, methods and methodologies. This paper presents an OLA platform called PANDA that is currently being developed as part of a German research and development project. The platform allows different learning systems to publish data about learners and their contexts, and applies different methods and techniques for information visualization and discovery to analyze the collected data and to detect interesting patterns within.

This paper describes a new method to model timing constraints for the generation of basic control functions for embedded test instruments in the area of structural testing of printed circuit boards. It describes how the timing information is extracted from data sheets, modeled in a domain specific language and processed to obtain the shortest possible test time for the automatically generated embedded test instrument. The generated hardware description of the test instrument is supplied as a co-processor to an embedded test-processor. This enables the processor to access the devices-under-test with correct and optimal timing, to speed up the test process and to allow at-speed testing.

Over the last decade, a large number of e-assessment system and tools were developed. However, they all provide assessments that are characterized by a static structure (ordered sequence of questions, predefined start and ending, etc.). This approach does not consider students individual knowledge or characteristics. Instead, each student has to answer the same questions. The work presented in this paper focuses on a personalized e-assessment system (askMe!) that not only identifies, but also supports and even compensates deficits in students' individual learning by considering students' strengths and preferences. In this paper, the core, the adaptation model of the system is presented that defines what can be adapted, as well as when and how it is to be adapted.

Over the last decade, a large number of e-assessment system and tools were developed. No matter whether they are stand-alone or integrated into learning management systems, they all have two things in common: (1) they do not consider individual aspects and (2) they do not require/encourage students to actively thinking, problem-solving, etc. The work presented in this paper focuses on the second aspect and provides a generic model that enables integrating Interactive Content Objects (ICOs) with established e-assessment systems and specifies the interactions with these objects. ICOs is the generic term for interactive tools, which do not restrict students to be passive recipients, but also engage them with material that is responsive to their actions. This results in more objective assessment findings and deeper learning, because students can learn from mistakes and make sense from unexpected situations.

Remote Engineering is one of the future directions for advanced teleworking/e-working environments not only in engineering and science (economics, informatics), but also in all other fields affecting society. In the last decade, considerable advances have been made regarding the design and development of remote and virtual laboratories, but it requires a lot of new specialists. As an attempt to improve the situation, several European universities started a joint Master Study Program in Remote Engineering (MARE) including in its curriculum a course of "Rapid Prototyping of Digital Systems" designed by the Ilmenau University of Technology in Germany. This course required an extensive use of laboratory equipment to carry out several exercises and therefore the implementation of an Online Lab turned out to be a good solution to ensure better learning outcomes.

The paper describes a new approach of boundary scan emulation based testing for adaptive failure diagnostics using programmable logic. The motivation to speed up boundary scan based testing as well as the approach taken for this new concept and architecture are presented. With this approach the possibilities of boundary scan testing can be extended by using the available on-board resources for a faster and more real-time oriented test. The new options and benefits, as well as the necessary fundamentals of this approach are indicated. An example and first test results are given as well, to indicate the advantage of the proposed system.

The paper describes how remote and virtual laboratories can support problem-based learning scenarios. We actually change from a traditional way of teaching, including lectures, seminars and hands-on laboratories, to a new, problem-based teaching approach. Therefore we use a developed multimedia environment. This environment consists of a set of Java applets, desktop applications and of a web based access to the hardware equipment which can be used in the classroom, for learning at home, in laboratory research and training, or for carrying out testing of students during exams. These educational tools support university courses on digital electronics, computer hardware, VLSI design and architectures, testing and design for testability of digital hardware. The tools are used to support a new approach to bachelor courses following a problem based method and organized as full-day courses.

The content, provided in Learning Management Systems (LMS), is often text oriented as in a usual textbook. We have added interactive components, such as smart design tools and online laboratories, in an LMS that allow exploring the learned content additionally to text parts interactively. The objective of our teaching concept is to empower the students to solve complex design tasks for digital systems and to validate the results. This requires at the one hand site knowledge about the mathematical background of the algorithms used by design tools and at the other hand side the experience from as much as possible examples. Commercial tools are too complex for teaching purposes and hide mostly the algorithms they use for the several design steps. Thatpsilas why we donpsilat use commercial tools. The connection to an LMS allows us to record all studentspsila relevant actions in the design process and to evaluate the student or to give individual tailored hints.