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.