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DCAD Databricks Certified Associate Developer for Apache Spark 3.0

Exam Details for DCAD Databricks Certified Associate Developer for Apache Spark 3.0:

Number of Questions: The exam consists of approximately 60 multiple-choice and multiple-select questions.

Time Limit: The total time allocated for the exam is 90 minutes (1 hour and 30 minutes).

Passing Score: To pass the exam, you must achieve a minimum score of 70%.

Exam Format: The exam is conducted online and is proctored. You will be required to answer the questions within the allocated time frame.

Course Outline:

1. Spark Basics:
- Understanding Apache Spark architecture and components
- Working with RDDs (Resilient Distributed Datasets)
- Transformations and actions in Spark

2. Spark SQL:
- Working with structured data using Spark SQL
- Writing and executing SQL queries in Spark
- DataFrame operations and optimizations

3. Spark Streaming:
- Real-time data processing with Spark Streaming
- Windowed operations and time-based transformations
- Integration with external systems and sources

4. Spark Machine Learning (MLlib):
- Introduction to machine learning with Spark MLlib
- Feature extraction and transformation in Spark MLlib
- Model training and evaluation using Spark MLlib

5. Spark Graph Processing (GraphX):
- Working with graph data in Spark using GraphX
- Graph processing algorithms and operations
- Analyzing and visualizing graph data in Spark

6. Spark Performance Tuning and Optimization:
- Identifying and resolving performance bottlenecks in Spark applications
- Spark configuration and tuning techniques
- Optimization strategies for Spark data processing

Exam Objectives:

1. Understand the fundamentals of Apache Spark and its components.
2. Perform data processing and transformations using RDDs.
3. Utilize Spark SQL for structured data processing and querying.
4. Implement real-time data processing using Spark Streaming.
5. Apply machine learning techniques with Spark MLlib.
6. Analyze and process graph data using Spark GraphX.
7. Optimize and tune Spark applications for improved performance.

Exam Syllabus:

The exam syllabus covers the following topics:

1. Spark Basics
- Apache Spark architecture and components
- RDDs (Resilient Distributed Datasets)
- Transformations and actions in Spark

2. Spark SQL
- Spark SQL and structured data processing
- SQL queries and DataFrame operations
- Spark SQL optimizations

3. Spark Streaming
- Real-time data processing with Spark Streaming
- Windowed operations and time-based transformations
- Integration with external systems

4. Spark Machine Learning (MLlib)
- Introduction to machine learning with Spark MLlib
- Feature extraction and transformation
- Model training and evaluation

5. Spark Graph Processing (GraphX)
- Graph data processing in Spark using GraphX
- Graph algorithms and operations
- Graph analysis and visualization

6. Spark Performance Tuning and Optimization
- Performance bottlenecks and optimization techniques
- Spark configuration and tuning
- Optimization strategies for data processing
Databricks Certified Associate Developer for Apache Spark 3.0
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DCAD Databricks Certified Associate Developer for Apache Spark 3.0

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Question: 386
Which of the following code blocks removes all rows in the 6-column DataFrame transactionsDf that have missing
data in at least 3 columns?
A. transactionsDf.dropna("any")
B. transactionsDf.dropna(thresh=4)
C. transactionsDf.drop.na("",2)
D. transactionsDf.dropna(thresh=2)
E. transactionsDf.dropna("",4)
Answer: B
Explanation:
transactionsDf.dropna(thresh=4)
Correct. Note that by only working with the thresh keyword argument, the first how keyword argument is ignored.
Also, figuring out which value to set for thresh can be difficult, especially when
under pressure in the exam. Here, I recommend you use the notes to create a "simulation" of what different values for
thresh would do to a DataFrame. Here is an explanatory image why thresh=4 is
the correct answer to the Question:
transactionsDf.dropna(thresh=2)
Almost right. See the comment about thresh for the correct answer above. transactionsDf.dropna("any")
No, this would remove all rows that have at least one missing value.
transactionsDf.drop.na("",2)
No, drop.na is not a proper DataFrame method.
transactionsDf.dropna("",4)
No, this does not work and will throw an error in Spark because Spark cannot understand the first argument.
More info: pyspark.sql.DataFrame.dropna - PySpark 3.1.1 documentation (https://bit.ly/2QZpiCp)
Static notebook | Dynamic notebook: See test 1,
Question: 387
"left_semi"
Answer: C
Explanation:
Correct code block:
transactionsDf.join(broadcast(itemsDf), "transactionId", "left_semi")
This QUESTION NO: is extremely difficult and exceeds the difficulty of questions in the exam by far.
A first indication of what is asked from you here is the remark that "the query should be executed in an optimized
way". You also have qualitative information about the size of itemsDf and transactionsDf. Given that itemsDf is "very
small" and that the execution should be optimized, you should consider instructing Spark to perform a broadcast join,
broadcasting the "very small" DataFrame itemsDf to all executors. You can explicitly suggest this to Spark via
wrapping itemsDf into a broadcast() operator. One answer option does not include this operator, so you can disregard
it. Another answer option wraps the broadcast() operator around transactionsDf the bigger of the two DataFrames.
This answer option does not make sense in the optimization context and can likewise be disregarded.
When thinking about the broadcast() operator, you may also remember that it is a method of pyspark.sql.functions. One
answer option, however, resolves to itemsDf.broadcast([]). The DataFrame
class has no broadcast() method, so this answer option can be eliminated as well.
All two remaining answer options resolve to transactionsDf.join([]) in the first 2 gaps, so you will have to figure out
the details of the join now. You can pick between an outer and a left semi join. An outer join would include columns
from both DataFrames, where a left semi join only includes columns from the "left" table, here transactionsDf, just as
asked for by the question. So, the correct answer is the one that uses the left_semi join.
Question: 388
Which of the elements that are labeled with a circle and a number contain an error or are misrepresented?
A. 1, 10
B. 1, 8
C. 10
D. 7, 9, 10
E. 1, 4, 6, 9
Answer: B
Explanation:
1: Correct C This should just read "API" or "DataFrame API". The DataFrame is not part of the SQL API. To make a
DataFrame accessible via SQL, you first need to create a DataFrame view. That view can then be accessed via SQL.
4: Although "K_38_INU" looks odd, it is a completely valid name for a DataFrame column.
6: No, StringType is a correct type.
7: Although a StringType may not be the most efficient way to store a phone number, there is nothing fundamentally
wrong with using this type here.
8: Correct C TreeType is not a type that Spark supports.
9: No, Spark DataFrames support ArrayType variables. In this case, the variable would represent a sequence of
elements with type LongType, which is also a valid type for Spark DataFrames.
10: There is nothing wrong with this row.
More info: Data Types Spark 3.1.1 Documentation (https://bit.ly/3aAPKJT)
Question: 389
Which of the following code blocks stores DataFrame itemsDf in executor memory and, if insufficient memory is
available, serializes it and saves it to disk?
A. itemsDf.persist(StorageLevel.MEMORY_ONLY)
B. itemsDf.cache(StorageLevel.MEMORY_AND_DISK)
C. itemsDf.store()
D. itemsDf.cache()
E. itemsDf.write.option(destination, memory).save()
Answer: D
Explanation:
The key to solving this QUESTION NO: is knowing (or reading in the documentation) that, by default, cache() stores
values to memory and writes any partitions for which there is insufficient memory
to disk. persist() can achieve the exact same behavior, however not with the StorageLevel.MEMORY_ONLY option
listed here. It is also worth noting that cache() does not have any arguments.
If you have troubles finding the storage level information in the documentation, please also see this student Q&A
thread that sheds some light here.
Static notebook | Dynamic notebook: See test 2,
Question: 390
Which of the following code blocks can be used to save DataFrame transactionsDf to memory only, recalculating
partitions that do not fit in memory when they are needed?
A. from pyspark import StorageLevel transactionsDf.cache(StorageLevel.MEMORY_ONLY)
B. transactionsDf.cache()
C. transactionsDf.storage_level(MEMORY_ONLY)
D. transactionsDf.persist()
E. transactionsDf.clear_persist()
F. from pyspark import StorageLevel transactionsDf.persist(StorageLevel.MEMORY_ONLY)
Answer: F
Explanation:
from pyspark import StorageLevel transactionsDf.persist(StorageLevel.MEMORY_ONLY) Correct. Note that the
storage level MEMORY_ONLY means that all partitions that do not fit into memory will be recomputed when they are
needed. transactionsDf.cache()
This is wrong because the default storage level of DataFrame.cache() is
MEMORY_AND_DISK, meaning that partitions that do not fit into memory are stored on disk.
transactionsDf.persist()
This is wrong because the default storage level of DataFrame.persist() is
MEMORY_AND_DISK.
transactionsDf.clear_persist()
Incorrect, since clear_persist() is not a method of DataFrame.
transactionsDf.storage_level(MEMORY_ONLY)
Wrong. storage_level is not a method of DataFrame.
More info: RDD Programming Guide Spark 3.0.0 Documentation, pyspark.sql.DataFrame.persist - PySpark 3.0.0
documentation (https://bit.ly/3sxHLVC , https://bit.ly/3j2N6B9)
Question: 391
"left_semi"
Answer: C
Explanation:
Correct code block:
transactionsDf.join(broadcast(itemsDf), "transactionId", "left_semi")
This QUESTION NO: is extremely difficult and exceeds the difficulty of questions in the exam by far.
A first indication of what is asked from you here is the remark that "the query should be executed in an optimized
way". You also have qualitative information about the size of itemsDf and transactionsDf. Given that itemsDf is "very
small" and that the execution should be optimized, you should consider instructing Spark to perform a broadcast join,
broadcasting the "very small" DataFrame itemsDf to all executors. You can explicitly suggest this to Spark via
wrapping itemsDf into a broadcast() operator. One answer option does not include this operator, so you can disregard
it. Another answer option wraps the broadcast() operator around transactionsDf the bigger of the two DataFrames.
This answer option does not make sense in the optimization context and can likewise be disregarded.
When thinking about the broadcast() operator, you may also remember that it is a method of pyspark.sql.functions. One
answer option, however, resolves to itemsDf.broadcast([]). The DataFrame
class has no broadcast() method, so this answer option can be eliminated as well.
All two remaining answer options resolve to transactionsDf.join([]) in the first 2 gaps, so you will have to figure out
the details of the join now. You can pick between an outer and a left semi join. An outer join would include columns
from both DataFrames, where a left semi join only includes columns from the "left" table, here transactionsDf, just as
asked for by the question. So, the correct answer is the one that uses the left_semi join.
Question: 392
Which of the following describes tasks?
A. A task is a command sent from the driver to the executors in response to a transformation.
B. Tasks transform jobs into DAGs.
C. A task is a collection of slots.
D. A task is a collection of rows.
E. Tasks get assigned to the executors by the driver.
Answer: E
Explanation:
Tasks get assigned to the executors by the driver.
Correct! Or, in other words: Executors take the tasks that they were assigned to by the driver, run them over partitions,
and report the their outcomes back to the driver. Tasks transform jobs into DAGs.
No, this statement disrespects the order of elements in the Spark hierarchy. The Spark driver transforms jobs into
DAGs. Each job consists of one or more stages. Each stage contains one or more
tasks.
A task is a collection of rows.
Wrong. A partition is a collection of rows. Tasks have little to do with a collection of rows. If anything, a task
processes a specific partition.
A task is a command sent from the driver to the executors in response to a transformation. Incorrect. The Spark driver
does not send anything to the executors in response to a transformation, since transformations are evaluated lazily. So,
the Spark driver would send tasks to executors
only in response to actions.
A task is a collection of slots.
No. Executors have one or more slots to process tasks and each slot can be assigned a task.
Question: 393
Which of the following code blocks reads in parquet file /FileStore/imports.parquet as a
DataFrame?
A. spark.mode("parquet").read("/FileStore/imports.parquet")
B. spark.read.path("/FileStore/imports.parquet", source="parquet")
C. spark.read().parquet("/FileStore/imports.parquet")
D. spark.read.parquet("/FileStore/imports.parquet")
E. spark.read().format(parquet).open("/FileStore/imports.parquet")
Answer: D
Explanation:
Static notebook | Dynamic notebook: See test 1,
Question: 394
Which of the elements that are labeled with a circle and a number contain an error or are misrepresented?
A. 1, 10
B. 1, 8
C. 10
D. 7, 9, 10
E. 1, 4, 6, 9
Answer: B
Explanation:
1: Correct C This should just read "API" or "DataFrame API". The DataFrame is not part of the SQL API. To make a
DataFrame accessible via SQL, you first need to create a DataFrame view. That view can then be accessed via SQL.
4: Although "K_38_INU" looks odd, it is a completely valid name for a DataFrame column.
6: No, StringType is a correct type.
7: Although a StringType may not be the most efficient way to store a phone number, there is nothing fundamentally
wrong with using this type here.
8: Correct C TreeType is not a type that Spark supports.
9: No, Spark DataFrames support ArrayType variables. In this case, the variable would represent a sequence of
elements with type LongType, which is also a valid type for Spark DataFrames.
10: There is nothing wrong with this row.
More info: Data Types Spark 3.1.1 Documentation (https://bit.ly/3aAPKJT)
Question: 395
"left_semi"
Answer: C
Explanation:
Correct code block:
transactionsDf.join(broadcast(itemsDf), "transactionId", "left_semi")
This QUESTION NO: is extremely difficult and exceeds the difficulty of questions in the exam by far.
A first indication of what is asked from you here is the remark that "the query should be executed in an optimized
way". You also have qualitative information about the size of itemsDf and transactionsDf. Given that itemsDf is "very
small" and that the execution should be optimized, you should consider instructing Spark to perform a broadcast join,
broadcasting the "very small" DataFrame itemsDf to all executors. You can explicitly suggest this to Spark via
wrapping itemsDf into a broadcast() operator. One answer option does not include this operator, so you can disregard
it. Another answer option wraps the broadcast() operator around transactionsDf the bigger of the two DataFrames.
This answer option does not make sense in the optimization context and can likewise be disregarded.
When thinking about the broadcast() operator, you may also remember that it is a method of pyspark.sql.functions. One
answer option, however, resolves to itemsDf.broadcast([]). The DataFrame
class has no broadcast() method, so this answer option can be eliminated as well.
All two remaining answer options resolve to transactionsDf.join([]) in the first 2 gaps, so you will have to figure out
the details of the join now. You can pick between an outer and a left semi join. An outer join would include columns
from both DataFrames, where a left semi join only includes columns from the "left" table, here transactionsDf, just as
asked for by the question. So, the correct answer is the one that uses the left_semi join.
Question: 396
Which of the elements that are labeled with a circle and a number contain an error or are misrepresented?
A. 1, 10
B. 1, 8
C. 10
D. 7, 9, 10
E. 1, 4, 6, 9
Answer: B
Explanation:
1: Correct C This should just read "API" or "DataFrame API". The DataFrame is not part of the SQL API. To make a
DataFrame accessible via SQL, you first need to create a DataFrame view. That view can then be accessed via SQL.
4: Although "K_38_INU" looks odd, it is a completely valid name for a DataFrame column.
6: No, StringType is a correct type.
7: Although a StringType may not be the most efficient way to store a phone number, there is nothing fundamentally
wrong with using this type here.
8: Correct C TreeType is not a type that Spark supports.
9: No, Spark DataFrames support ArrayType variables. In this case, the variable would represent a sequence of
elements with type LongType, which is also a valid type for Spark DataFrames.
10: There is nothing wrong with this row.
More info: Data Types Spark 3.1.1 Documentation (https://bit.ly/3aAPKJT)
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Databricks Databricks learner - BingNews https://killexams.com/pass4sure/exam-detail/DCAD Search results Databricks Databricks learner - BingNews https://killexams.com/pass4sure/exam-detail/DCAD https://killexams.com/exam_list/Databricks To Become Adventurous Learners, Kids Need Routine

Learning often requires taking different risks, whether it’s the willingness to try something new or trying after a failed attempt. For children, it’s this process of learning how to take risks and becoming comfortable with failure that can help them grow and develop. But encouraging them to take these risks, even when it’s scary and uncomfortable, can be a difficult task for parents. As research is showing, the willingness of a child to take risks in learning can depend on what their relationship with their parents look like.

Taking risks while learning  

In a recent study, conducted by researchers at the University of Wisconsin-Madison, children who viewed their parents as being reliable were more willing to take risks while learning. In this study, which included more than 150 children, participants were asked questions about their home environment, which included their relationship with their parents, after which they were asked to play a series of games.  

Children who viewed their parents as being more reliable, which included answering yes to questions such as whether they could count on them to pick them at specific times, follow through on their promises, or predict their reaction to different situations, were more likely to take risks during the games.  

“The children from more stable backgrounds, they play around and experiment in our games. They use that to get a sense of how things work, maybe earning them more money or more points,” said Seth Pollak, a psychologist at University of Wisconsin-Madison and the lead researcher on the study, in a press release.  

Having parents who are seen as reliable can be thought of as a buffer for children, one that gives them the security to take risks and explore. “If you trust your parent is there, you trust the reliability and the stability, it allows you to venture off and come back,” says Sarah Greenberg, executive director of behavior change and expertise for Understood.org, which is a nonprofit dedicated to supporting people with learning differences. “It’s almost like this internalized feeling of a safety net.” 

It’s this sense of reliability and predictability that gives kids the sense that it’s okay to take risks and to fail, as they have a parent at home that they can count on, who will be there to support them.  

Look for patterns of behavior

Creating a supportive learning environment for your child often includes identifying what they struggle with and what they need. One way to do that is to track certain behaviors over time, looking for patterns. “Your child can’t necessarily tell you what they need, but they are often showing you,” Greenberg says.  

For example, if your child is consistently having a meltdown after school, that could be a sign that they are overwhelmed or overstimulated from the school day, and need some extra time to decompress before starting their homework. Other behaviors could include refusals to do something, such as writing with a pencil or doing their math homework, which may be a sign they are struggling in specific areas.  

Small, consistent routines make a difference

Oen way to create consistency and reliability, even when swamped with all of the day-to-day demands of raising a family, is to develop small, but consistent routines with your child. “One positive routine can be a really good starting point,” Greenberg says.  

In terms of these routines, it’s less about how big or time-consuming they are, and more about their predictability. For example, it could be making the effort to give them 10 minutes of undivided attention when they come home from school, making it a habit to play LEGOs with them every Friday evening, or a predictable bedtime routine. “Ten or 20 minutes of consistent, positive attention can make such a world of difference,” Greenberg says.  

The key is to make it consistent and enjoyable, to give your child a sense that their parents are there for them. “It’s not about the rigidity, it’s really about the solidity, that the child feels the ground beneath them,” Greenberg says.  

Thu, 14 Dec 2023 10:00:00 -0600 en text/html https://lifehacker.com/family/help-kids-become-adventurous-learners
Learner driver failed 59 theory tests before pass No result found, try new keyword!A learner driver who failed the theory test 59 times before passing has been praised for their "amazing" commitment. The person, who has not been named, spent £1,380 and around 60 hours on the ... Sun, 03 Dec 2023 10:00:00 -0600 en-us text/html https://www.msn.com/ Are fast learners a myth?

Are some children quick studies? Carnegie Mellon University conducted a study of people believed to be rapid learners to see if their strategies might help all children. The rub is that the researchers could not find any faster learners, as the Hechinger Report noted.

After studying the learning rates of 7,000 children, and adults, using instructional software and educational games, scientists found no evidence that some people progress faster than others. 

As it happens, all students actually needed practice to learn something new, and they also learned about the same amount from each practice. High and low achievers alike needed about seven to eight practice sessions to learn a new concept.

“Students are starting in different places and ending in different places,” said Ken Koedinger, a cognitive psychologist and director of Carnegie Mellon’s LearnLab, where the research was conducted, “But they’re making progress at the same rates.” 


Tue, 28 Nov 2023 19:19:00 -0600 en text/html https://edsource.org/updates/are-fast-learners-a-myth
Education

English learners might have been hit especially hard during the pandemic and need extra targeted support, experts and advocates say. But some school district leaders aren’t yet concerned about the data.

Results from 2023 state tests show English learners are further behind their peers from 2019 compared with other student groups, and they’re struggling more to get back on track.

On the main state tests in English language arts and math, the biggest falloff in proficiency between 2019 and this year is for English learners. They also showed less growth. Of those taking the SAT and PSAT for example, only students with disabilities showed less growth.

Helping English learners recover from the pandemic has been a complex problem nationwide.  And test scores aren’t the only warning sign about how English learners in Colorado schools are faring: While nearly a third of Colorado students were chronically absent last year, for example, 40% of English learners missed enough school to get that label. In Colorado, English learners make up 12% of all K-12 students. Some districts have much higher concentrations than others.

Full story via Chalkbeat 

Get more Colorado news by signing up for our Mile High Roundup email newsletter.

Tue, 07 Nov 2023 20:26:00 -0600 Yesenia Robles en-US text/html https://www.denverpost.com/2023/11/08/test-scores-english-learners-covid-school-districts/
An Ecosystem of Trust

Almost four in 10 current college students (38 percent) have transferred at least once in their six-year academic career, and 40 million students nationwide have some college credits but no degree. Engaging these students toward degree completion isn’t a new challenge in higher education, but it is one that can now be meaningfully advanced through technology. What if learners and students had more access to their accomplishments to better communicate their educational history? What frictions could we reduce and what new value could grow?

This learning mobility is just as important to institutions as it is to students—according to the “State of Digital Credentials in the AACRAO Community” Report, 83 percent of respondents considered learning mobility a priority at their institutions.

We’re now in a moment of tremendous opportunity to impact learning and earning mobility through the power of verifiable digital credentialing, which can be simply expressed as agency—agency over the receiving, saving and sharing of records of achievement placed into the hands of learners. The Trusted Learner Network is laser-focused on designing an ecosystem of solutions that put learners at the center and create space for credential-enabled innovation.

About the Trusted Learner Network

The TLN has been engaged in exploring solutions and opportunities in the digital credential ecosystem since 2019, when Lev Gonick, Donna Kidwell and Phillip Long from Arizona State University presented the originating concept of the TLN at Educause: to meet the challenge of navigating the lifelong learning journey, students and learners need to have access to and control over the many types of accomplishments they’ll earn in their lives. Along with this assertion came a set of key principles to guide the work to come: equity, interoperability, consent and stewardship needed to be at the heart of any development to honor the commitments and responsibilities of both institutions and learners.

Emerging from this foundation, the TLN is focused on three key strategies:

  1. Creating governance to guide digital credentialing,
  2. Building a community to learn and grow, and
  3. Developing institutional and learner technologies to enable verifiable credentialing

Today, the TLN’s primary mission is to recognize and enable the value of the credentials created, shared and distributed as being valid and useful in serving the needs of learners. As mentioned, 38 percent of college students transfer before earning their degree. Verifiable credentials are data-rich, consent-governed and can be shared directly by the learner with their institution of choice. What’s more, these credentials allow for streamlined admissions processes and hiring, because institutions and employers can instantly verify their validity through cryptographic proofs.

It’s All About Trust

Central to the development of the TLN initiative has been the discovery of how crucial trust is to the ecosystem of digital credentials. While technology was an initial driver of the work—that blockchain technologies could allow us to issue a credential that a learner could manage and share independently and verifiably—trust is core to the process of all credentialing, be it credit transfer, degree verification, certification processes or any assertion of learning and achievement.

  • Does the recipient of a credential trust the achievement being shared?
  • Do they trust the authority of the credential source?
  • Does that trust translate into value?

Current technology achieves these aims through a series of closed systems that must exist outside of the learner’s control. Verifiable credential technology allows for that trust chain to remain unbroken, even when we put sharing directly in the hands of the learners.

As we have established, credentials are no longer just issued by degree-granting institutions. Students are now engaging with higher education institutions and learning with alternative credit providers, companies that are creating their own internal learning models and through paid or free online content. In light of this, the higher education community has a unique opportunity to recognize and embrace diverse learning such as knowledge, skills and abilities (KSAs). To build trust, standards need to be developed and embraced so that there is consistency in these crucial processes and recognition of practices over time.

This is where the TLN work becomes crucial.

Why Governance Guides All

Because equity is at the heart of everything we do, the work of the TLN is guided by the diverse voices and perspectives of TLN’s governing body. These groups include individuals from different institution types—four-year colleges and universities, community colleges, public and private, urban and rural—to make sure that we’re designing a network that benefits all institutions and students/learners.

Together, the TLN governing body aims to create an environment that is guided by strong governance policies to ensure that institutions can clearly understand and place trust in verifiable credential technology, while simultaneously placing learners at the center of important decision making. To date, the committee has collaborated to create TLN’s Governance Charter, as well as TLN’s Issuance Guide and Learner Policies.

Moving Forward

For the TLN to Strengthen transfer and credit mobility for the benefit of learners, it will take dedication, participation and commitment by many who are part of the credentialing community. We recognize that we don’t have all the answers, and that is why partnership in this space is key. Going on five years, the TLN has convened experts, evangelists, technologists and advocates who are committed to moving the digital credential ecosystem forward.

For more information and to join the TLN community, visit tln.asu.edu.

Many thanks to TLN governing body members Noah Geisel of the University of Colorado at Boulder, Insiya Bream of the University of Maryland Global Campus, Meena Naik of the Jobs for the Future Foundation and Sherri Braxton of Bowdoin College for contributing to this article.

Wed, 06 Dec 2023 10:00:00 -0600 en text/html https://www.insidehighered.com/opinion/blogs/beyond-transfer/2023/12/07/ecosystem-trust-learners
Shifting Student Support to Adult Learners No result found, try new keyword!Colleges are increasingly seeking to attract and retain nontraditional-age learners. However, meeting their needs means rethinking student-support services, which have long catered to students age ... Mon, 24 Jul 2023 04:52:00 -0500 en-US text/html https://www.chronicle.com/events/virtual/shifting-student-support-to-adult-learners English Language Learner Writing Center

The mission of the English Language Learner Writing Center (ELLWC) is to provide one-on-one consulting to undergraduate and graduate multilingual student writers. Through collaborative peer interaction, supportive and well-trained consultants help students, whose first language is not English, become more confident users of English academic language and effective, autonomous writers across genres and disciplines. We ensure opportunities for student writers to understand and practice multiple proofreading, self-editing, and polishing strategies as well as Strengthen their English language skills necessary for achieving academic and professional success.

Tue, 22 Aug 2023 15:02:00 -0500 en text/html https://miamioh.edu/centers-institutes/english-language-learner-writing-center/index.html
Learner drivers kill woman, 21, in horror smash after losing control while racing ‘side by side’, court hears

LEARNER drivers killed a woman, 21, in a horrific crash after losing control while racing "side by side" on the road, a court heard.

Jago Clarke and Emma Price are on trial at Swansea Crown Court after care home worker Ella Smith tragically lost her life in a gruesome wreckage in Broad Haven, Pembrokeshire.

Ella Smith, 21, was killed in a crash after two learner drivers raced 'side by side' a court heard

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Ella Smith, 21, was killed in a crash after two learner drivers raced 'side by side' a court heardCredit: WNS
Ella with her dad Adrian and mum Maria

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Ella with her dad Adrian and mum MariaCredit: WNS

The victim, who died in June 2021, was sitting in the passenger seat while Clarke, 21, raced "competitively" with Price, also 21, the court was told.

A jury heard Clarke was spotted drinking Budweiser as well as mixing a concoction of vodka and Sourz before getting behind the wheel of Ella's Ford Ka.

Meanwhile, Price drove her Citroen C1 side by side down the B4341 in a race with her pal.

Price's car was not involved in the crash but her driving in the moments leading up to Ella's death makes her equally responsible for the tragedy, it has been argued in court.

The court heard that disaster struck when Clarke swerved out of control and hit the kerb which sent his car "twisting and turning" onto the opposite lane.

In moments, his vehicle had smashed into an oncoming vehicle, the court heard.

Prosecutor Jim Davis told the jury: "Neither defendant should have been driving either vehicle at the time, as both only held provisional licences and were not being supervised.

"It seems that Jago Clarke was bragging to everyone that he was going to drive Ella's car and overtake everyone."

Another pal, Luis Heathfield also claimed he begged Clarke not to drive, the court heard.

Mr Heathfield said he shouted: "Don't be a f****** idiot" when Clarke took a seat behind the wheel and Ella went to the passenger side.

The witness also told the court he saw both cars swerving and thought Price had been trying to prevent Clarke from overtaking.

Meanwhile, the driver of the oncoming car involved said he could see two vehicles "side by side" barrelling towards him at a "very fast" speed.

Rowan Fair told the court he saw Ella's car hit a hedge and "fly back out" into the road in front of him.

His partner, and passenger, Daisy Buck was left seriously injured as a result of the collision.

She told the jury there was a "horrendous impact" moments before realising she had a large leg wound.

Clarke, of Hubberston, Milford Haven, denied causing death by dangerous driving and causing serious injury by dangerous driving.

He accepted a lesser charge of causing death by careless driving and admitted causing death by driving while unlicensed and uninsured.

Price, of Holloway, Haverfordwest, denied causing death by dangerous driving, causing death by driving while unlicensed and uninsured and causing serious injury by dangerous driving.

The trial continues.

Jago Clarke and Emma Price are on trial at Swansea Crown Court

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Jago Clarke and Emma Price are on trial at Swansea Crown CourtCredit: WNS
Wed, 13 Dec 2023 23:07:00 -0600 Summer Raemason en-gb text/html https://www.thesun.co.uk/news/25052390/learner-drivers-kill-woman-crash-trial-ella-smith/
Video: Driver with learner’s permit crashes through eye care shop in Burlington
Local News
A car crashed through an eye care store in Burlington Tuesday morning. Burlington Police Department

A car crashed through an eye care store in Burlington Tuesday morning, strewing the scene with shattered glass in an incident that was captured on video.

Burlington police responded to the crash at about 10:45 a.m. They were called to All Eye Care Doctors located at 85 Middlesex Turnpike, near the Burlington Mall.

First responders found a 2019 Mitsubishi Outlander that appeared to have driven into the store before crashing into another business, the furniture store Relax the Back, according to Burlington police. A building inspector also responded to the scene.

There were no reported injuries, and both stores are currently closed so that repairs can be made.

Police said they determined that an adult woman with a learner’s permit was behind the wheel. The crash was apparently accidental, and no charges are being filed.

Security footage from the eye care store obtained by Boston 25 News shows the car crashing into the store and knocking over at least one display case. Someone inside the store can be seen approaching the car before a woman and a man exit the vehicle.

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Tue, 12 Dec 2023 10:00:00 -0600 en-US text/html https://www.boston.com/news/local-news/2023/12/13/video-driver-with-learners-permit-crashes-through-eye-care-shop-in-burlington/
Learner driver fails 59 theory tests before pass

A learner driver who failed the theory test 59 times before passing has been praised for their "amazing" commitment.

The person, who has not been named, spent £1,380 and around 60 hours on the process at a test centre in Redditch.

That is more than anyone else in Britain, but only just: another learner in Hull failed 57 tests, another in Guildford 55, and a driver in Tunbridge Wells fell short 53 times.

The AA said nerves always play a part but that "revision is key to success".

The figures from the Driver and Vehicle Standards Agency (DVSA) relate to learner drivers who passed during the first half of 2023.

Each theory attempt costs £23 and takes around an hour.

Camilla Benitz, managing director of AA Driving School - which has launched a revision app helping learners prepare for the test, said: "There's no doubt it's a tough test and these learners' commitment to passing is amazing.

"It's quite easy to underestimate the theory test," she said, urging the importance of revision.

Department for Transport figures show the pass rate for theory tests has fallen from 65% in the 2007-2008 financial year to 44% in 2022-2023.

Learners must pass the theory before they can book a practical driving test in the UK.

The theory test consists of 50 multiple-choice questions testing candidates' knowledge of the Highway Code and guidance on driving skills, for which at least 43 correct answers are required.

This is followed by a hazard perception test, which involves 14 video clips of driving situations.

Sun, 03 Dec 2023 17:06:00 -0600 en-GB text/html https://www.bbc.co.uk/news/uk-67610152




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