MOOCdb: Developing Data Standards for MOOCs

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The development of Massive Open Online Courses (MOOCs) has stimulated educational research and challenged the existing models of distance and online learning. New questions have emerged that could be answered and administered across thousands of students. Although data on the engagement of this large number of online learners is quite accessible for researchers, they are usually limited to few sources. Very few researchers have data from different courses, institutions and/or platforms. Student privacy and data protection regulations tend to limit data sharing and suppress collaborative research that is necessary for addressing some of the main challenges MOOC research is currently facing. The MOOC Data Science Commons is a collaborative platform that tends to bring the next generation of MOOC research, allowing for generalized MOOC data organization and shared analytics. The aim of the project is to bring together educational researchers, computer science researchers, machine learning researchers, technologists, database and big data experts to advance MOOC data science.

Science

MOOCdb:
Developing Data Standards for MOOCs
Srećko Joksimović
s.joksimovic@ed.ac.uk
@s_joksimovic
Kalyan Veeramachaneni
kalyan@csail.mit.edu
Dragan Gašević
dragan.gasevic@ed.ac.uk
FutureLearn Academic Network Conference
15 June 2015

Data sources
SQL_anonymized_forum.sql
SQL_anonymized_general.sql
SQL_hash_mapping.sql
standard extract.csvSQL_unanonymizable.sql.gz
clickstream_export.gz
Personal data
Demographic data
…

$Data sources Weekly data packages auth_user-{site}-analytics.sql auth_userprofile-{site}-analytics.sql certificates_generatedcertificate-{site}-analytics.sql Daily data packages course_structure-{site}-analytics.json courseware_studentmodule-{site}-analytics.sql email_opt_in-{site}-analytics.csv student_courseenrollment-{site}-analytics.sql user_api_usercoursetag-{site}-analytics.sql user_id_map-{site}-analytics.sql {org}-{course}-{date}-{site}.mongo wiki_article-{site}-analytics.sql wiki_articlerevision-{site}-analytics.sql {org}-{site}-events-{date}.log.gz.gpg$

Challenges
• Analytics across several courses
• Analytics across different platforms
• Analytics across different institutions
• Sharing data

Solution?
• Collaborative data science platform
– Standardize data storage
– Generalizable across courses and data providers
(currently OpenEdX, edX and Coursera)
– “Data being shared without data being
exchanged”
– Sharing and reproducing the results

MOOCdb
Observing mode
- Observed Events table
- Resources table
- Resources Types table
- URLs table
- Resource URLs table
Submitting mode
- Problem Types table
- Problems table
- Submissions table
- Assessments table
Collaborating mode
- Collaborations table
- Collaboration Types table
Feedback mode
- Feedbacks table
- Questions table
- Answers table
- Surveys table
User information
- User PII table
- Global User table
- Course User table
http://moocdb.csail.mit.edu/wiki/index.php?title=MOOCdb

Collaborative platform and
applications
edX Coursera MOOCdb doc Github repo
Feature factory LabelMe Digital learner quantified Problem analytics
My MOOCViz Social network analysis Forum analysis Dropout prediction

Current state
• Established network of institutions
– MIT, Stanford, University of Michigan, University
of Edinburgh, University of Queensland, University
of Texas (Austin)
• Release of open source software
• Development and release of the first data
analytics framework

Next steps
Digital Learner Quantified
Discussion forum analysis
LabelMe
Problem analytics
Dropout prediction
Social network analysis

Collaboration
• If you are interested in…
– Development
– Feature modeling
– Translating your data
– Testing
kalyan@csail.mit.edu
s.Joksimovic@ed.ac.uk

Q&A
MOOCdb:
Developing Data Standards for MOOCs
Srećko Joksimović
s.joksimovic@ed.ac.uk
@s_joksimovic
Kalyan Veeramachaneni
kalyan@csail.mit.edu
Dragan Gašević
dragan.gasevic@ed.ac.uk
FutureLearn Academic Network Conference
15 June 2015

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HotJupiters are amongthebest-studied exoplanets, but it is still poorly understood how their chemical composition and cloud properties vary with longitude. Theoretical models predict that clouds may condense on the nightside and that molecular abundances can be driven out of equilibrium by zonal winds. Here we report a phase-resolved emission spectrum of the hot Jupiter WASP-43b measured from 5–12µ 5–12µ 5–12µm with JWST’s Mid-Infrared Instrument (MIRI). 1524 ±35 1524 ±35 and 863±23 The spectra reveal a large day–night temperature contrast (with average brightness temperatures of 1524 ± 35 863 ±23 863 ±23Kelvin, respectively) and evidence for water absorption at all orbital phases. Comparisons with three-dimensional atmospheric models show that both the phase curve shape and emission spectra strongly suggest the presence of nightside clouds which become optically thick to thermal emission at pressures greater than ∼100mbar. The dayside is consistent with a cloudless atmosphere above the mid-infrared photosphere. Con3trary to expectations from equilibrium chemistry but consistent with disequilibrium kinetics models, methane is not detected on the nightside (2σ upper limit of 1–6 parts per million, depending on model assumptions).

Nightside clouds and disequilibrium chemistry on the hot Jupiter WASP-43b

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Jupiter’s moon Io hosts extensive volcanism, driven by tidal heating. The isotopic composition of Io's inventory of volatile chemical elements, including sulfur and chlorine, reflects its outgassing and mass loss history, and thus records information about its evolution. We used millimeter observations of Io’s atmosphere to measure sulfur isotopes in gaseous SO2 and SO, and chlorine isotopes in gaseous NaCl and KCl. We find 34S/32S = 0.0595 ± 0.0038 (equivalent to δ34S = +347 ± 86‰), which is highly enriched compared to average Solar System values and indicates that Io has lost 94 to 99% of its available sulfur. Our measurement of 37Cl/35Cl = 0.403 ± 0.028 (δ37Cl = +263 ± 88‰) shows that chlorine is similarly enriched. These results indicate that Io has been volcanically active for most (or all) of its history, with potentially higher outgassing and mass-loss rates at earlier times.

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Context. WASP-76 b has been a recurrent subject of study since the detection of a signature in high-resolution transit spectroscopy data indicating an asymmetry between the two limbs of the planet. The existence of this asymmetric signature has been confirmed by multiple studies, but its physical origin is still under debate. In addition, it contrasts with the absence of asymmetry reported in the infrared (IR) phase curve. Aims. We provide a more comprehensive dataset of WASP-76 b with the goal of drawing a complete view of the physical processes at work in this atmosphere. In particular, we attempt to reconcile visible high-resolution transit spectroscopy data and IR broadband phase curves. Methods. We gathered 3 phase curves, 20 occultations, and 6 transits for WASP-76 b in the visible with the CHEOPS space telescope. We also report the analysis of three unpublished sectors observed by the TESS space telescope (also in the visible), which represents 34 phase curves. Results. WASP-76 b displays an occultation of 260±11 and 152±10 ppm in TESS and CHEOPS bandpasses respectively. Depending on the composition assumed for the atmosphere and the data reduction used for the IR data, we derived geometric albedo estimates that range from 0.05 ± 0.023 to 0.146 ± 0.013 and from <0.13 to 0.189 ± 0.017 in the CHEOPS and TESS bandpasses, respectively. As expected from the IR phase curves, a low-order model of the phase curves does not yield any detectable asymmetry in the visible either. However, an empirical model allowing for sharper phase curve variations offers a hint of a flux excess before the occultation, with an amplitude of ∼40 ppm, an orbital offset of ∼−30◦ , and a width of ∼20◦ . We also constrained the orbital eccentricity of WASP-76 b to a value lower than 0.0067, with a 99.7% confidence level. This result contradicts earlier proposed scenarios aimed at explaining the asymmetry observed in high-resolution transit spectroscopy. Conclusions. In light of these findings, we hypothesise that WASP-76 b could have night-side clouds that extend predominantly towards its eastern limb. At this limb, the clouds would be associated with spherical droplets or spherically shaped aerosols of an unknown species, which would be responsible for a glory effect in the visible phase curves.

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Past civilisations have nurtured small populations of those trying to understand and manipulate nature to some advantage in materials, tools, weapons, food, and wealth. However, they never formed communities and lacked the means of recording, communicating, and sharing successes and failures. They also lacked a common framework/philosophy to qualify them as scientists, but that all began to change in the 16th Century. In this lecture we consider the progression to a philosophy of science, and the underlying principles and assumptions that now guide scientific inquiry.We also examines the nature of scientific knowledge, the methods of acquisition, evolution, and significance over past centuries, and reflect on the value to society. In the struggle to solve problems, deliver understanding, and reveal the truth about our universe, science had to suffer and survive: ignorance, bigotry, established superstitions, and the ‘diktats’ of religions and politics, and latterly, falling education standards mired by social media. We chart that ‘scientific’ journey emphasising the importance of observation, experimentation, and the search for universal laws. Ultimately, this essentially Aristotelian perspective was challenged and overtaken by the rise of empiricism, which emphasised the importance of sensory experience and the limitations of human knowledge. Science continues to evolve and provide us with the best truths attainable with our leading edge technologies of observation and experimentation. Today, it stands as the greatest and richest contributor to human knowledge, understanding, progress, and wellbeing. In turn, debates and controversies are ongoing, shaping the field and philosophy which remains essential for understanding the nature of scientific knowledge and the models it creates. But unlike any belief system, the answers and models furnishers by science are not certain and invariant, they tend to be stochastic and incomplete - ‘the best we can do’ at a given time.

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Cotton crops are vulnerable to a variety of sucking pests, which can severely impact plant health, yield, and fiber quality. These pests primarily feed on plant sap, extracting nutrients directly from the plant's vascular system. Here's a breakdown of some of the most significant sucking pests in cotton cultivation: Aphids: Cotton aphids or melon aphids can cause direct damage by sucking sap and indirect damage by secreting honeydew, which encourages sooty mold growth. This can interfere with photosynthesis and weaken the plant. Aphids can also transmit viral diseases. Whiteflies: Two species, the silverleaf whitefly and the bandedwinged whitefly, are particularly troublesome. They not only suck sap from the underside of leaves, causing yellowing and leaf drop, but their honeydew excretion promotes sooty mold and they can transmit several plant viruses. Thrips: While thrips can chew on plants, their primary damage to cotton is through sucking. They attack the cotton plant during its seedling stage, which can stunt growth and reduce vigor. Thrips are also capable of transmitting the Cotton Bud disease. Spider Mites: These are not insects but arachnids. Spider mites, such as the two-spotted spider mite, suck cell contents from the leaves, leading to speckled discoloration and potentially significant leaf loss if infestations are severe. Leafhoppers: Including various species, leafhoppers can cause direct damage through feeding, which results in leaf curling and stunted growth. They can also be vectors for plant diseases. Mealybugs: These pests are less common but can be problematic, especially in clustered planting conditions. They suck sap and secrete honeydew, which leads to sooty mold. Mealybugs can also spread viruses. Stink Bugs: Although primarily known for their chewing mouthparts, certain stink bugs can cause damage similar to sucking pests by injecting saliva into the plant and sucking out nutrients, leading to boll damage and stained lint. Management Strategies: Cultural Controls: This includes practices such as crop rotation, using resistant varieties, and managing planting and harvesting times to avoid peak pest populations. Biological Controls: Beneficial insects like lady beetles, lacewings, and predatory mites can naturally control sucking pest populations. Parasitic wasps also play a role in controlling aphid and whitefly populations. Chemical Controls: Insecticides may be used but should be chosen carefully to minimize resistance development and preserve beneficial insects. Systemic insecticides can be particularly effective against sucking pests. Integrated Pest Management (IPM): Combining multiple control strategies based on monitoring and thresholds to apply the most effective and environmentally sensitive approach. Effective management of sucking pests in cotton requires a thorough understanding of the pest species present, their life cycles, and the ecological balance of the field environment.

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The primary distinction between a test cross and a backcross is that a test cross is used to determine the genotype of a phenotypically dominant individual, whereas a backcross is used to recover a dominant genotype from a parent who has an extreme genotype. The hybrids that are backcrossed are known as 'BC' hybrids. For example, a BC1 hybrid is an F1 hybrid that was crossed with one of its parents or a genetically similar species. The BC2 hybrid is defined as a BC1 hybrid that has been crossed with the same parent or genetically similar species. Other instances include backcrossing in animals. A system of breeding in which repeated backcrosses are made to transfer a specific character to a well-adapted variety for which the variety is deficient is referred to as backcross breeding This approach is infrequently utilized in vegetatively propagated crops such as sugarcane and potatoes, and only with slight alterations. A test cross is a genetic technique for determining an unknown genotype in a dominant person. It is a breeding procedure in which a (known genotype) homozygous recessive individual is paired with an individual of the opposite mating type who has an unknown dominant genotype. The phenotypic characteristics of the resulting children are investigated, and the genotype of the examined individual is determined appropriately. If all of the progeny from the test cross are dominant, we may conclude that the genotype of the tested unknown person is homozygous dominant. If 50% of kids exhibit dominant traits and the remaining 50% exhibit recessive traits, we may conclude that the genotype of the tested unknown individual is heterozygous dominant. #genetics #backcross #testcross #mendel #crosses #monohybridtestcross #backcrossandtestcross #typesofcross #dihybridtestcross #limitationsofcross #applicationsofcross #mscbotany #botany

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A botany file is similar to a chemistry practical file but specifically focuses on experiments and observations related to plants. It typically includes detailed descriptions of plant-related experiments, such as germination studies, growth experiments, tissue culture techniques, plant anatomy investigations, and ecological surveys. The file may contain records of plant specimens collected, their classification, characteristics, and ecological significance. Additionally, it may include diagrams, photographs, drawings, tables, and data analysis related to plant research. Like a chemistry practical file, a botany file serves as a documentation of practical work conducted in the field of botany and is often used for assessment and educational purposes.

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Understanding circumstellar disks is of prime importance in astrophysics, however, their birth process remains poorly constrained due to observational and numerical challenges. Recent numerical works have shown that the small-scale physics, often wrapped into a sub-grid model, play a crucial role in disk formation and evolution. This calls for a combined approach in which both the protostar and circumstellar disk are studied in concert. Aims. We aim to elucidate the small scale physics and constrain sub-grid parameters commonly chosen in the literature by resolving the star-disk interaction. Methods. We carry out a set of very high resolution 3D radiative-hydrodynamics simulations that self-consistently describe the collapse of a turbulent dense molecular cloud core to stellar densities. We study the birth of the protostar, the circumstellar disk, and its early evolution (< 6 yr after protostellar formation). Results. Following the second gravitational collapse, the nascent protostar quickly reaches breakup velocity and sheds its surface material, thus forming a hot (∼ 103 K), dense, and highly flared circumstellar disk. The protostar is embedded within the disk, such that material can flow without crossing any shock fronts. The circumstellar disk mass quickly exceeds that of the protostar, and its kinematics are dominated by self-gravity. Accretion onto the disk is highly anisotropic, and accretion onto the protostar mainly occurs through material that slides on the disk surface. The polar mass flux is negligible in comparison. The radiative behavior also displays a strong anisotropy, as the polar accretion shock is shown to be supercritical whereas its equatorial counterpart is subcritical. We also f ind a remarkable convergence of our results with respect to initial conditions. Conclusions. These results reveal the structure and kinematics in the smallest spatial scales relevant to protostellar and circumstellar disk evolution. They can be used to describe accretion onto regions commonly described by sub-grid models in simulations studying larger scale physics.

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MOOCdb: Developing Data Standards for MOOCs

1. MOOCdb: Developing Data Standards for MOOCs Srećko Joksimović s.joksimovic@ed.ac.uk @s_joksimovic Kalyan Veeramachaneni kalyan@csail.mit.edu Dragan Gašević dragan.gasevic@ed.ac.uk FutureLearn Academic Network Conference 15 June 2015

2. Data sources SQL_anonymized_forum.sql SQL_anonymized_general.sql SQL_hash_mapping.sql standard extract.csvSQL_unanonymizable.sql.gz clickstream_export.gz Personal data Demographic data …

3. Data sources Weekly data packages auth_user-{site}-analytics.sql auth_userprofile-{site}-analytics.sql certificates_generatedcertificate-{site}-analytics.sql Daily data packages course_structure-{site}-analytics.json courseware_studentmodule-{site}-analytics.sql email_opt_in-{site}-analytics.csv student_courseenrollment-{site}-analytics.sql user_api_usercoursetag-{site}-analytics.sql user_id_map-{site}-analytics.sql {org}-{course}-{date}-{site}.mongo wiki_article-{site}-analytics.sql wiki_articlerevision-{site}-analytics.sql {org}-{site}-events-{date}.log.gz.gpg

4. Challenges • Analytics across several courses • Analytics across different platforms • Analytics across different institutions • Sharing data

5. Solution? • Collaborative data science platform – Standardize data storage – Generalizable across courses and data providers (currently OpenEdX, edX and Coursera) – “Data being shared without data being exchanged” – Sharing and reproducing the results

6. MOOC data science commons

7. MOOCdb Observing mode - Observed Events table - Resources table - Resources Types table - URLs table - Resource URLs table Submitting mode - Problem Types table - Problems table - Submissions table - Assessments table Collaborating mode - Collaborations table - Collaboration Types table Feedback mode - Feedbacks table - Questions table - Answers table - Surveys table User information - User PII table - Global User table - Course User table http://moocdb.csail.mit.edu/wiki/index.php?title=MOOCdb

8. Collaborative platform and applications edX Coursera MOOCdb doc Github repo Feature factory LabelMe Digital learner quantified Problem analytics My MOOCViz Social network analysis Forum analysis Dropout prediction

9. Current state • Established network of institutions – MIT, Stanford, University of Michigan, University of Edinburgh, University of Queensland, University of Texas (Austin) • Release of open source software • Development and release of the first data analytics framework

10. Next steps Digital Learner Quantified Discussion forum analysis LabelMe Problem analytics Dropout prediction Social network analysis

11. Collaboration • If you are interested in… – Development – Feature modeling – Translating your data – Testing kalyan@csail.mit.edu s.Joksimovic@ed.ac.uk

12. Q&A MOOCdb: Developing Data Standards for MOOCs Srećko Joksimović s.joksimovic@ed.ac.uk @s_joksimovic Kalyan Veeramachaneni kalyan@csail.mit.edu Dragan Gašević dragan.gasevic@ed.ac.uk FutureLearn Academic Network Conference 15 June 2015

Editor's Notes

MOOCdb which is our solution to centralizing and generalizing MOOC data organization and providing general purpose analytics for MOOC education research.
“How does amount of time spent on the videos during a certain week correlate to performance on the homework?”
CAN WE HAVE STANDARDIZED DATA STORAGE? Sharing and reproducing the results: When they publish research, analysts can share the scripts by depositing them into a public archive where they are retrievable and cross-referenced to their donor and publication.
The MOOCdb project aims to brings together educational researchers, computer science researchers, machine learning researchers, technologists, database and big data experts to advance MOOC data science. The project founded at MIT includes a platform agnostic functional data model for data exhaust from MOOCs, a collaborative-open source-open access data visualization framework, a crowd sourced knowledge discovery framework and a privacy preserving software framework. The team is currently working to release a number of these tools and frameworks as open source. WHAT MOOCdb PROVIDES? Concise data storage: MOOCdb's proposed schema is \loss-less" with respect to research relevant information, i.e. no information is lost in translating raw data to it. Access Control Levels for Anonymized Data: The data schema offers an organized means of structuring anonymized user identities safeguard them further.
Sharing of data extraction scripts: Scripts for data extraction and descriptive statistics extraction can be open source and shared by everyone because they reference data organized according to the schema. Crowd source potential: Machine learning frequently involves humans identifying explanatory variables that could drive a response. Enabling the crowd to help propose variables could greatly scale the com- munity's progress in mining MOOC data.

MOOCdb: Developing Data Standards for MOOCs

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