Bildungsinformatik

Overview of the Educational Computer Science at the DIPF

Dr. Daniel Schiffner

Overview

  • Who is Who
    • DIPF
    • EduCS Team
    • Tasks and Infrastructures
  • Research and Infrastructures
    • AI in Learning and Assessment
    • APIs for OpenScience
    • AI in Information Systems
  • Future Work

Who is Who

Structure

Bildungsinformatik (Educational Computer Science / EduCS)

  • Unit at the DIPF | Leibniz-Institute for Research and Information in Education
  • Part of the department "Information Center Education"
    • Staff: 118 persons
    • 20 projects with 5 M€ in 2022
    • Approx 180 publications, 58 peer-reviewed in journals and conferences
    • Approx 75 talks

Educational Computer Science Group (Bildungsinformatik)

  • Provide research infrastructures
  • Maintain and develop systemarchitectures
  • Research on
    • Open science
    • Digitalization of education

Who is Who

Structure

Lead

  • Dr. Daniel Schiffner
    • Diplom @ GU 2009 (with honors)
    • Dr. phil. nat. 2012 @ GU
    • Head of R&D of studiumdigitale 2014
    • Head of Educational Computer Science @ DIPF 2019
    • Teaching
      • 2009 - 2024 → Goethe University
      • Tutoring & thesis mentoring [approx 130 BSc + MSc]
      • Since 2012 → Own teaching
        • HCI, Visualization, Animation, eLearing
        • Project Management, Computer Graphics
    • Speaker of Technology-based Assesment Center
    • Founder of AK Open Science
    • Founder of AG Softwareentwicklung within Leibniz
    • Long-term PC member: EC-TEL, DELFI

Seniors

  • Dr. Christian Richter
  • Dr. Jan Schneider
  • Dr. Claudia Melania Chituc
  • N.N.

Other staff (29 persons)

  • Programmers: 15
  • Operations: 4
  • PhDs: 5
  • Project Managers: 2
  • Assistances: 3

Base / Permanent Budget: approx 1.6 M€ / a

Who is Who

Structure

Tasks and Infrastructures

  • Operations and maintenance of hardware / servers
  • Development and improvement of information systems
    • Deutscher Bildungsserver (DBS) → 20k unique visitors / day
    • IWWB → 4k unique visitors / day
    • ComPleTT
    • MYSKILLs NEXT
  • Literaturdatenbank Fachportal / FIS Bildung
    • Over 1 million Open Access documents
    • German education sector
    • Mastodon: eduresearch.social
  • Forschungsdaten Bildung / Research data center
    • Data storage, archiving and exchange
    • APIs
    • NFDI
  • EduTec
    • AI in Education
    • Teaching Master Educational Technologies
      with Prof. Drachsler, Prof. Kiesler and Prof. Krömker
  • Technology-based Assessment
    • NEPS - National Educational Panel Study
      Long-term study with 70.000 participants
      in Germany
    • Multi-Document Comprehension Assessments
  • Third-Party Funding and Research

Information Center Education

Research and Infrastructures

What are Research Infrastructures (RIs)?

  • Support and foster researchers in their tasks
  • Enhance the current state of RIs by applying own research
  • Evaluate and push technologies

EduCS

  • Connection to educational sector → User-Centered Design
  • Experience in software development and maintenance
  • Research on the technology used in education

AI in Assessment

Multi-Modal Data - Definition

  • Use multiple, independent sensors
  • Gather and refine data
  • Process to provide feedback while doing / exercising

Challenges

  • Distributed systems
  • Heterogenous hardware
  • Limited bandwidth
    • Edge-computing
  • Feedback for users and teachers
  • Research on
    • Automated feedback generation
    • Efficient data collection

AI in Assessment

Multi-Modal Data - Applications

  • Presentation trainer presentationtrainer
  • Multi-Modal games and apps backstraight,doh
  • Data analysis and evaluation mmla
Presentation Trainer presentationtrainer
"Get Your Back Straight" backstraight: Evaluation of the app created
Data-Analysis mmla: Is there a difference in the data?

AI in Assessment

Systemarchitectures and -design

Definition and general challenges with assessments

  • High-stakes assessment
  • Provide reliable testing assessment
  • Guarantees for display and answers
  • Curated editing process elicitation

MYSKILLs

  • Project at DIPF
  • Initiated with Bundesagentur für Arbeit
  • Experimentation with Bildungsträger
  • Online assessment with 100-200 parallel exams
  • Refugees and retraining

Assessment

Systemarchitectures and -design

Old architecture

  • Monolithic application based on TAO 2.5 (old!)
  • Failover required prior duplication of tests (and much more)
  • Each server instance could handle max 50 exams

MYSKILLs NEXT architecture

  • Follow-Up project (Internal Funding: 2 FTE)
  • Distributed system with load-balancing only on critical loops
  • Decoupling from monitoring and execution
  • Event-driven system
  • Public APIs and token based authorization system (custom)
  • Offline and adaptive testing

Assessment

Systemarchitectures and -design

MYSKILLs NEXT architecture

The MYSKILLs NEXT Architecture: Assessment as a Service

Assessment

Systemarchitectures and -design

DeCoS - Delivery Content Store

Main challenge:

There must be explicit lists of validated and versioned documents, i.e. snapshots of tests
  • git-based JSON files
  • GraphQL interface
  • Partial schema(less)
  • NodeJS server

Alternatives Considered:

  • Redis
  • MongoDB
  • Both would require a custom index to be maintained!

Learning and Assessment

ComPleTT

Common Platform for electronic Teacher Training

  • Working environment
  • Used in all 16 states of Germany!
  • Collection and distribution of courses and media

Impact and Outreach

  • Started 2020 with 3 states (RLP, HE and BY)
  • Currently over 800 persons registered
  • Creating and sharing materials for teacher education
  • Permanent funding via KMK approved in 2022
  • Strong connection to lernen:digital (BMBF Project, 2 FTE)

APIs for OpenScience

Definition

  • OpenScience requires to follow several principles
    • We use FAIR
  • Allows for free exchange of knowledge

Challenges

  • Recursive, especially if using FAIR digital objects (FDO)
    • Abort criterion in A2 is "weak"
      Datasets tend to degrade or disappear over time because there is a cost to maintaining an online presence for data resources. When this happens, links become invalid and users waste time hunting for data that might no longer be there. Storing the metadata generally is much easier and cheaper. Hence, principle A2 states that metadata should persist even when the data are no longer sustained. A2 is related to the registration and indexing issues described in F4.
  • Assumption that metadata stays "fixed" is unfeasible
  • Access might / must be still restricted

Uniform access might enable more sharing

APIs in OpenScience

NFDI

Konsort SWD

  • Consortium on social, behavioral, educational and economic sciences
    Konsort SWD
  • Development of APIs for data exchange (TA5-M3, 3.2 FTE)

EduCS furthermore has

  • Connections via RDCs
  • Relation to NFDIxCS
  • Strong partnership with other Leibniz institutes regarding metadata

APIs in OpenScience

Examples

  • KonsortSWD: FAIR API konsortswd
  • OpenData in EdTec delfi_opendata,iticse_opendata
  • What is a good API? api
  • Enhancement of tools rdmo
Shared and safe data space (Presented at GO-INTER)
FDO model used as a building block konsortswd
What are important criteria for an API api

Computer Science Education

Definition

  • Teaching can be supported using computer technology
  • Especially computer science education lacks good tools
  • ChatGPT / Co-Pilot have a huge impact

Challenges

  • Data is not "selectable" → What sources are used
  • Data is not "clean" → Good / bad examples
  • Features of current ai systems
  • DevUX

Comparable with assessment

  • What is good feedback?
  • How to adapt to student's performance

Plan of EduCS

  • Use infrastructure to run and evaluate
  • Reach out to "try" it in the field
DALL-E prompt: Generate a picture of a young computer-science student programming with AI

Computer Science Education

Examples

  • Impact on computer science assessments llm-edu
  • How to use chatbots finding a course in IWWB maze
  • How to design a system nlp
How good can ChatGPT solve programming tasks llm-edu
Visualization of available offers in the region [IWWB-PLUS]
Chatbots for lifelong learning maze

Other research topics at EduCS

Selection of topics

  • Data visualization (OpenLAIR, IWWB-PLUS dashboard) connecting,indicators
  • Data protection and security (FDB/NFDI)
  • Data distribution and information / access sharing (lernen:digital, ComPleTT) complett,vigor
  • Open educational resources and quality (OERinfo)
  • Knowledge Graphs

Projects

Overview

Name Funder Own Funding Role
lernen:digital (2023-2026) BMBF 525k€ Co-PI (with Prof. Rittberger)
NEPS TBT (2023-2027) LIfBi 205k€ Co-PI (with Prof. Goldhammer)
OERinfo (2023-2028) BMBF 370k€ Co-PI (with Prof. Rittberger)
ComPleTT (2022-) KMK 115k€ / year PI
TAEPS (2022) DIE 16k€ PI
IWWB-PLUS (2021-2024) BMBF 637k€ Co-PI (with Prof. Rittberger)
SchuMaS (2021-2026) BMBF 385k€ Co-PI (with Prof. Goldhammer, Prof. Rittberger, Prof. Maaz)
KonsortSWD (2020-2025) BMBF / DFG 465k€ Co-PI (with Prof. Wolf)
ComPleTT (Feasability Project, 2020) Digitalpakt 35k€ PI
Adult Basic Literacy Skills Denmark (2020) VIVE 28k€ PI
QualiBi (2020-2023) DFG 163k€ Co-PI
PIAAC Cycle 2 (2019-2023) OECD 84k€ PI
The Next Level (2019-2022) BMBF 338k€ Co-PI (with Prof. Horz)
CATS (2016-2019) BMBF 210k€ Co-PI (with Prof. Krömker)

Future Work

Increase involvement in Working Groups

  • Leibniz AG for software development
  • AK GI OpenScience

Projects

  • ComPleTT + lernen:digital
  • IWWB-PLUS → Scientific Use-Files
  • AI and Infrastructures → Support manual collection of metadata
  • MMLA → Seamless integration into learning environments
  • APIs and data visualizations

Literature

Thank you for your attention

Teaching

Lectures and exercises

  • Connection to research
  • Small projects for students
  • Basis for theses (just hit 130 persons recently)
  • Positive feedback

Teaching evaluation

Profillinie FB12: Evaluation result for HCI (N=28, WS18/19)
Profillinie FB12: Evaluation result for "Plattformen und Systeme fürs eLearning" (N=10, WS22/23)
Profillinie FB12: Evaluation result for "Plattformen und Systeme fürs eLearning" (N=8, WS23/24)