Digipedagogical guidelines

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EXAM for electronic individual exams

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EXAM service and electronic exam concept provides a flexible way of taking monitored exams and maturity exams in four HAMK campuses. Additional instructions can be found at https://wiki.eduuni.fi/display/CSCEXAM/EXAM+for+students.

Opening hours

HAMK students can take their exams Mon-Sun @ 08.00-21.30 in HAMK EXAM studios.
NB! You need an access tag to enter Valkeakoski campus at all hours.

HAMK EXAM studios are closed from mid-June until the end of July. Please check the opening hours of campus doors

House rules of the exam studio

  • Store your belongings in a locker outside the studio.
  • You are not allowed to leave the exam room before completing the exam. (No toilet visits!)
  • You are not allowed to talk with other students in the exam room.
  • Do not walk or move unnecessarily during your exam.
  • There is a recording camera and microphone surveillance system in the exam room which is used to supervise students taking the exam. Records may also be checked afterwards if there is any reason to suspect cheating.
  • Sanctions will be imposed against any student who is found in breach of examination regulations.

Enrolling the exam

  1. The teacher prepares the exam either for the whole study group or personally for someone or creates a maturity test.
    • The exam availability is adjusted and during that period a student can take the exam whenever he/she wants in any of HAMK EXAM studios.
  2. You log into the EXAM service exam.hamk.fi, search the right exam, register for the exam and book an exam time. If your teacher prepares an individual exam you’ll receive an email with a link for booking the exam time.
    • The exams start only on the hour.
    • The EXAM studios are located in Hämeenlinna, Riihimäki, Valkeakoski and Forssa campuses.
  3. You will receive a confirmation email with instructions for entering the EXAM studio. The instruction includes the number for the computer spot.

Editing, cancelling or removing the enrollment

If you are not able to do the exam in the reserved time you must cancel the booking. A booking that has not been cancelled will be counted as an attempt.

  1. Log into the EXAM service exam.hamk.fi (use your username only)
  2. You’ll see your future bookings in the dashboard. From three dots you’ll be able to edit or cancel your reservation. If you want to remove the booking you have to cancel your booking first.
  3. If the EXAM computer has been left open by the prevoius exam-taker, restart the computer.

Entering the campus

  • When a HAMK student has an access card to use on campus doors: the exam time is free to choose from the EXAM times available. No separate access card or such is required to enter EXAM studio.
  • If HAMK students don’t have an access card, they choose an EXAM time slot during office hours on weekdays for the EXAM studio of Hämeenlinna University Centre to ensure passage through the campus doors.
    • If necessary, in Hämeenlinna students can obtain the access tag from HAMKO’s office. At other campuses, ask the campus information desk.
  • NB! In Valkeakoski you always need an access tag.
HAMKOn opiskelijakortti exam
Kulkukortti exam

Entering the studio

  1. Before the exam occasion, make sure your username works and you have access through the outside doors of the campus. All EXAM studios are always accessible. When the campus doors are locked, you may need a pin-code that you have placed in the locks in addition to the access tag.
  2. Store your belongings in a locker outside the studio. You must not take anything else to the studio other than just a water bottle and an ID in addition to a face mask.
    • If you have special needs, your teacher must be informed before enrolling on the exam.
  3. Enter the studio not earlier than five minutes before the scheduled time.
    • EXAM room video surveillance system includes live streaming, recording video and audio.

Logging in to EXAM computers

login to exam computers

Taking the EXAM

  • When you have logged in and take the exam, there is a timer that shows you the time left.
  • You can answer the questions in any order.
  • The system autosaves your answer within a few minutes intervals. Also, moving between questions autosaves the answer. Note that if you delete your answer, the system may autosave the empty answer!
  • When you are ready, return to the overview. Be sure to attach any attachments before you finish the exam.
  • Click on the “Submit! “button.
  • Remember to shut down the computer.

After the exam

After taking the exam the teacher will assess it.

What to do in problem situations?

  • The building is closed: have you checked the opening hours? Unregister and make a new registration. When needed, contact your examiner.
  • The computer does not start or does not work properly: check all cables and restart.
  • The browser (Firefox) does not start: Restart the computer.
  • The system gives an error message: close the browser and retry a few times. If the problem continues, contact the janitor. Wait for the information about whether to start the exam or not.
  • The browser or computer jams or crashes in the middle of the exam: Restart the computer and browser, log in and continue with the exam.

Exam visit

  • Exam visit means that you take your own university’s exam in another university’s EXAM room.
  • HAMK students log in to HAMK’s EXAM, register for the exam, choose another university’s EXAM room as their exam position, and book exam time. Printed exams are not available on exam visits.
  • Before booking a time for your exam in another university, please check the university’s rules and regulations as well as opening hours and how to access the exam room. List of universities where exam visits are possible: e-exam visit, web page.
  • When you go to take your exam, you log in to the other university’s EXAM with your own university’s user id and password.
  • Further instructions for making an exam room reservation from an external institution: Exam consortium’s instructions for an exam visit (web page).
  • NB!
    • Each university defines the programs available in their EXAM rooms. It is the student’s responsibility to find out if the other university has the programs needed for your exam.
    • HAMK paper exams cannot be taken as exam visits in the EXAM rooms of other universities.

EXAM accessibility statement

HAMK EXAM system accessibility statement

The stages of creating module implementation

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Building a module implementation

You can design, build, and develop implementations using a ten-step model with your module team.

  • In the planning phase, the implementation plan, sketch and script help to outline the whole of the implementation, the goals and clarify the learning process (steps 1-3).
  • During the construction of the learning process, you may ask for feedback and test the functionality of the implementation and evaluate the implementation with quality criteria (steps 4-7).
  • The development of the module is based on feedback received during and after the implementation and reflection of the teaching team. It allows you to improve the learning experience and the quality of implementation (steps 8-10).

Designing a module implementation (pdf-format).

A model for constructing a module implementation in text format

  1. The implementation plan is the basis for the work Make the 1st version of the implementation plan.
  2. The draft is a joint design of the content Plan the mission of the module in cooperation with the teaching team. Teams can work on documents, share materials, hold online meetings and manage tasks.
  3. The script is the design of the learning process
    In collaboration with your teaching team, write the module script. The teams use Word, PowerPoint, Excel, Forms, OneNote and Planner.
  4. Building the learning process Build your learning process in Moodle. You can use the following tools to produce your content: Teams OneNote, PowerPoint, Word, Excel, ScreenPal, Kaltura Capture, Zoom, Venngage, H5P, ThingLink, Wonda, Webropol and various professional applications.
  5. Ask for feedback and make corrections Ask for feedback from colleagues or students, e.g. in Teams. Use different sets of quality criteria as a framework for evaluation.
  6. Refining the implementation plan
    Refine the implementation plan. Add a Moodle workspace for the implementation. Students are approved in Peppi.
  7. Finish building your learning environment Build your content and learning process in Moodle. Materials can be located in Kaltura, Teams (Word, PowerPoint, Excel, OneNote, Stream) or elsewhere online.
  8. Implementation makes a reality
    Guide students in Moodle, Zoom, Teams and Viva Engage. Hold online sessions in Zoom or Teams. Collect quick feedback from students through polls during implementation.
  9. Collect feedback from students
    Evaluate the implementation with the teaching team according to the quality criteria. Students give final feedback on the module.
  10. Develop a module for the next implementation
    Käsittele toteutuksen palaute opettajatiimin kanssa. Valikoi ja toteuta kehittämisideat.

Content

Recording Teams meetings and sharing the recording

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Learn how to record a Teams meeting and what’s related to it.

Recording Teams-session

The session can be easily recorded in Teams. When you start recording, the transcription of speech also begins. This means that speech is automatically typed into text. Transcription can be used in video subtitles. The language of transcription can be selected from numerous options. Make sure the language is the same as the one spoken in the meeting. If necessary, you can also turn off the transcription completely.

Other things to know about recording

This Microsoft guide goes into more detail about recording a Teams meeting, e.g.

  • Who can record ameeting?
  • Where is the session recorded?
  • Recording expiration

More guidance

Arene’s recommendations on the use of artificial intelligence for universities of applied sciences

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Note! These recommendations have been prepared by the Arene working group, and they are not joint guidelines for universities of applied sciences. Universities of applied sciences prepare their own guide-lines independently.

The recent rapid development of artificial intelligence has placed universities of applied sciences in a situation where they must consider the role of artificial intelligence extensively, as part of the learning process and as a working life skill. Arene recommends that universities of applied sciences operate at two different levels:

  1. at the organisational level, universities of applied sciences are encouraged to ensure the capa-bility of the staff and students to use artificial intelligence responsibly
  2. at the level of teaching, teachers are encouraged to ensure that AI is used in accordance with its purpose and in an ethical manner

Arene also recommends universities of applied sciences to support, guide and advise students in the use of AI.

Arene will monitor the development of generative artificial intelligence and AI-assisted technologies and update this guidance as necessary.

Recommendations for the organisational level of universities of applied sciences

At the organisation’s management level, universities of applied sciences must enable responsible use of AI tools for teachers, staff and students. Instructions must be prepared regarding the use of AI tools and their use must be encouraged primarily by the tools offered by the organisation.

Universities of applied sciences must take the following into account when using artificial intelligence:

  • Ethical principles: the use of AI tools must be based on fairness, equality, equity and respect for others.
  • Responsibility: Artificial intelligence tools must promote students’ learning and the development of working life skills.
  • Data protection: The use of AI tools must not endanger the data protection or privacy of staff or students.
  • Competence: Ensure that staff and students have competence in the basic use of artificial intelligence tools by providing instructions and training.
  • Transparency: Universities of applied sciences must ensure that the operating principles and decisionmaking processes of AI tools are openly visible and understandable to all users. This pro-motes trust and enables a critical assessment of the use of artificial intelligence.

In relation to artificial intelligence tools, universities of applied sciences must act as follows at the organisational level:

  • Enable: Artificial intelligence tools must be available, and their use must be instructed to both staff and students.
  • Guide: The use of AI tools must be in accordance with good scientific practice.
  • Promote equality: The use of AI tools must not affect the equal treatment of students, staff or other stakeholders.
  • Share information: Communicate AI tool capabilities, limitations and uses to stakeholders.
  • Train / support competence development: Universities of applied sciences must train students and staff in the responsible use of AI tools.
  • Ensure / Manage risks: The use of AI systems involves risks of leaking sensitive information and copyright infringements. Universities of applied sciences must identify data protection risks and process sensitive data appropriately.
  • Follow developments in this field: Monitoring the development of artificial intelligence technology as a university of applied sciences and awareness of new practices affect the use of artificial intelligence in universities of applied sciences. If necessary, the university of applied sciences assesses and updates its ethical and operating instructions to correspond to the latest trends and best practices. Whenever possible, when participating in a wider discussion on the ethical use of artificial intelligence and participating in initiatives by organisations in the field to promote the responsible use of artificial intelligence, the university of applied sciences influences the development of the matter at the national level.
  • Monitor use: By collecting feedback on the use of artificial intelligence through an open channel and reporting on flaws, the university of applied sciences promotes openness and develops the use of artificial intelligence in its community.

Educational institutions should take into account the impact of artificial intelligence on learning processes and theses and initiate discussions from the perspective of specific industries and working life.

Recommendations for the teachers of universities of applied sciences

Teachers must understand the opportunities of artificial intelligence in teaching and learning and further develop their teaching to correspond to the age of artificial intelligence.

Teachers at universities of applied sciences play an important role in teaching working life skills. Artificial intelligence is one of the tools of working life. Teachers must ensure that students graduating from universities of applied sciences have the competence to use artificial intelligence tools.

In the teaching of universities of applied sciences, the following must be taken into account when using artificial intelligence:

  • Understanding: Teachers must understand what to do with artificial intelligence applications in teaching and learning and how these can support learning and streamline day-to-day life.
  • Responsibility: The teacher must use artificial intelligence responsibly and ensure that its use pro-motes students’ learning and development. The author is always responsible for their output.
  • Ethical principles: The teacher must observe general ethical principles, such as fairness, equal treatment and respect for other students and teachers.
  • Data protection: The teacher must follow the data protection practices of the university of applied sciences also when using artificial intelligence tools.
  • Restrictions: Artificial intelligence systems are only programs and have limitations. The teacher must be aware of these restrictions to assess the suitability of the use of artificial intelligence in different situations.

In the teaching activities of universities of applied sciences, the use of AI tools must strengthen students’ working life skills, which is why teachers are recommended to

  • Encourage: Positively encourage students to use AI as part of their studies.
  • Guide: By guiding students, the appropriate and responsible use of artificial intelligence is ensured. Instruct students on how to use artificial intelligence in a manner suitable for each course.
  • Utilise: By using artificial intelligence tools to support the planning, evaluation and guidance of teaching, the teacher increases their competence and understanding of the opportunities and limitations of artificial intelligence.
  • Participate: The teacher should share their knowledge of the capabilities, limitations and uses of the tool in their university of applied sciences community. The teacher, when participating in the discussion on the ethical use of AI in their university of applied sciences community and participating in initiatives related to AI in school communities and organisations, promotes the responsible use of AI.
  • Pay attention to specific industries: Teachers should familiarise themselves with the development in their industry and related examples and apply the information to their own teaching. Teach-ers should share their experiences of operating models and practices in their industry.
  • Apply: Teachers should take into account the impact of artificial intelligence on study assignments, learning processes and theses from the perspective of individual industries and working life.

The recommendations in appendix 1 contain optional examples of AI guidelines for teachers.

Arene’s traffic light model in assignments

Teachers are recommended to include Arene’s traffic light model clauses on the use of AI in learning assignments. The Arene traffic light model icons and text phrases can be found in English and Finnish under the Templates4U button in Moodle’s text editor. Detailed instructions: Digipedagogical guideline Moodle Text Editor assignment template.

Students

Students should understand the possibilities of artificial intelligence in their studies and develop their competence.

Using AI tools can enhance learning and make the learning experience more multidimensional. How-ever, it should be noted that students are always responsible for the content of their study assignments and the materials to be assessed. When using AI tools, the student must pay attention to the following:

  • Understanding: Students must understand the opportunities of AI applications in the promotion of learning and how these can support learning and streamline day-to-day life.
  • Responsibility: Students must develop their AI literacy and have a critical view of AI output as the author is always responsible for their own work.
  • Knowledge: Artificial Intelligence Systems are only programs and have limitations, and artificial intelligence does not have competence or understanding in content. Be aware of these limitations so that you can assess the suitability of the use of artificial intelligence in different situations.
  • Ethical principles: Observe general ethical principles, such as fairness, equal treatment and re-spect for other students and teachers.

Students at universities of applied sciences are encouraged to use artificial intelligence in order to develop their own working life skills.

  • Adopt: Use artificial intelligence skillfully as an assistant and support in learning.
  • Give feedback: discuss and give feedback on the success of AI to the teacher.
  • Join the conversation and share knowledge: be part of the UAS community, discuss the ethical use of AI and participate in initiatives by your school communities and organisations to promote the responsible use of AI.
  • Report: Quickly report any errors and problems related to the use of artificial intelligence in teaching.

Students at universities of applied sciences are recommended to take the instructions related to artificial intelligence into account in their thesis.

  • In order to supplement their competence, students can use different AI services to create ideas, build a knowledge base and search for information. However, students must take into account any instructions related to cheating in their higher education institution.
  • Students should acknowledge that cheating includes dishonestly presenting any ideas, processes, results or words as their own that have been produced using services such as essay writing soft-ware and ghost writers, or technology such as AI writers and generators.

Introduction to recommendations

These recommendations concern the use of generative artificial intelligence, which has made considerable progress in recent years. Today, generative artificial intelligence can produce credible images, videos, sound and text that approaches human-made works. In the future, generative artificial intelligence will develop further, and its areas of use will expand significantly. This enables, for example, the development of individual learning environments and the production of accessible materials and ser-vices that adapt to individual needs, for example through multilingualism. In addition, generative artificial intelligence enables better personal assistance through dialogue interaction in the future. For example, voice-controlled virtual assistants respond to increasingly complex questions and provide in-dividual responses.

The role of universities of applied sciences in ensuring working life competence

These recommendations concern the use of generative artificial intelligence, which has made considerable progress in recent years. Today, generative artificial intelligence can produce credible images, videos, sound and text that approaches human-made works. In the future, generative artificial intelligence will develop further, and its areas of use will expand significantly. This enables, for example, the development of individual learning environments and the production of accessible materials and ser-vices that adapt to individual needs, for example through multilingualism. In addition, generative artificial intelligence enables better personal assistance through dialogue interaction in the future. For example, voice-controlled virtual assistants respond to increasingly complex questions and provide in-dividual responses.

Artificial intelligence = Supportive intelligence and its ethical use

Artificial intelligence should rather be referred to as supportive intelligence. Artificial intelligence is not “intelligent” and, for example, applications do not have an understanding of their content, even though they may serve as an assistant, a brainstormer, a sparring partner, a mentor and a booster. The grammatically correct and ostensibly sensible output creates a misconception of the intelligence of the application and the correctness of the answer, even though it may be completely distorted in terms of content. In other words, it does not eliminate the need for substance competence from the user, but instead emphasises it so that we can ensure that the information produced by artificial intelligence is correct. Artificial intelligence reflects the training data entered into it. Possible inaccuracy of the in-formation, i.e., hallucination in the response, must be borne in mind as a restriction on use. In addition, it may provide biased or harmful information, which is due to the training data used in the teaching of the language model. The majority of the source material used to teach today’s language model comes from Western countries. The fact that materials from all countries have not been equally used as source material weakens the quality of the application and its ability to produce objective information, taking into account different cultures. The responsibility for the accuracy of the produced information rests with the user of artificial intelligence. In utilizing artificial intelligence, users must develop their AI literacy, i.e. their ability to understand and critically assess the activities and outputs of artificial intelligence. Artificial intelligence itself does not care whether or not something is true.

The discussion around artificial intelligence is strongly related to ethics. For example, the European Union has prepared its own ethical guidelines on artificial intelligence. These guidelines have previously been largely linked to the development of artificial intelligence. The ethical principles of the use of artificial intelligence can be derived from good general scientific practices at universities of applied sciences:

  1. The authors are responsible for the accuracy, correctness, integrity and originality of their works, including the use of artificial intelligence.
  2. AI does not fulfil the author’s requirements, taking into account accountability.
  3. Scientific writing practices must be followed in the use of artificial intelligence. The works must be the author’s own, and they must not present the ideas, information, words or other material of others without sufficient reference. Artificial intelligence is not a source of scientific text. The author must ensure that the citations are correct.
  4. The content produced by AI can be biased and damaging or strengthen existing harmful stereotypes. The author must always take ethical perspectives into account.

Appendix 1 – Example of instructions for teachers on the use of artificial intel-ligence

Required, must be used, must be reported, affects assessment Artificial intelligence must be used to create outputs*. The student must report how he/she has used AI. Failure to use AI will affect the assessment.
Prohibited, not to be used The output must be created without the help of artificial intelligence. The student should use only their own knowledge, understanding and skills. The use of AI is forbidden for a justified reason and will be interpreted as fraud.
Allowed, can be used, must be reported, may affect assessment Artificial intelligence can be used in the creation of outputs, but the student must clearly report its use. Failure to disclose the use of AI will be interpreted as fraud. The use of AI may affect the assessment.
Allowed, can be used, need not be reported Artificial intelligence can be used freely and without report to create the output. The use of AI does not affect the assessment.

* Output means the final work or competence produced by a student that meets the objectives of the given learning task. This may be an essay, research report, presentation, project or other concrete work that demonstrates the student’s understanding and application of the subject matter.

Artificial intelligence (AI)

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Concepts

Artificial Intelligence (AI) is according to Wikipedia‘s definition, it is a computer or a computer program capable of performing actions considered intelligent. A more precise definition of artificial intelligence is open, because intelligence itself is difficult to define.

Artificial Narrow Intelligence (ANI) is a type of artificial intelligence designed to perform a specific task or set of tasks. It is also known as weak artificial intelligence or adaptive artificial intelligence. All artificial intelligence systems in use today, such as voice assistants Alexa and Siri, Tesla’s driving assistant or ChatGPT, are so-called narrow artificial intelligence applications.

Artificial General Intelligence (AGI) is a type of artificial intelligence capable of learning any intellectual task performed by a person. It is a hypothetical concept that has yet to be achieved in practice, but is often used as a benchmark when evaluating the capabilities of current AI systems. Learning to drive a car, cook, analyze a large amount of data, any work task independently is the goal of all development.

Generative AI is a branch of machine learning that uses algorithms and data to create something new. Let’s think about painting, for example. You have a bunch of paintings – impressionism, cubism, realism, etc. – and you want the AI to create its own work of art. A generative artificial intelligence that has studied these styles is able to create its own work of art, which is new and unique, but which takes influences from the studied styles.

Generative AI works with two main components: a generator and a discriminator. The generator creates new, authentic-feeling results, such as paintings in our example. The discriminator, on the other hand, evaluates these creations and compares them to the original learned models – it tries to distinguish real artworks painted by real artists from images created by artificial intelligence. The generator then tries to improve its creations based on the discriminator’s feedback until it produces something that the discriminator thinks is the right piece.

This process is like an evolving game where the generator and the discriminator compete with each other. This “game” helps AI learn and develop new, creative ideas that can mimic or even surpass original designs. And this is the essence of generative AI: it not only learns to understand data, but also creates new, innovative ideas based on what it has learned.

Artificial intelligence = Support intelligence

We should rather talk about artificial intelligence as support intelligence. It functions excellently as an assistant, ideator, sparring partner, mentor, enhancer, etc. However, it does not remove the subject matter expertise from the user but, on the contrary, emphasizes it, so that we can verify the accuracy of the information produced by artificial intelligence. It also supports independent thinking.

Language models

Language models (LLM) are the ‘engines’ of generative artificial intelligences, which, among other things, can read, summarize, and translate texts (Watch a video about the technology behind language models (YouTube, opens in a new window)). They are capable of processing human-generated text. They predict future characters in a string based on probabilities formed through machine learning, allowing them to create sentences similar to those spoken and written by humans. The task of a language model is thus to generate human-like, fluent text based on the input (prompt) given to it. The most common way to provide input to an AI application is through text typed into a text field.

A grammatically correct and reasonable-sounding text creates an illusion of the correctness of the answer, even though it may be completely distorted. So substantial knowledge of the subject is still needed. Often, checking the accuracy of the text written by artificial intelligence and marking the sources is more laborious than actually producing the text based on the sources.

Artikkeli: Näin ChatGPT syntyi – kukaan ei täysin ymmärrä, miten kielimallit toimivat (Tivi 7.9.2023) – avautuu HAMK:n tunnuksilla (only in Finnish)

The language model is not intelligent

A Language model is just a program that can generate text based on probabilities based on the source material in response to the input it is given. It has no knowledge or understanding of the content, although a fluent and grammatically correct answer may give a vague picture. The responsibility for the correctness of the written information rests with the artificial intelligence user.

Language models can handle prompt content in dozens of languages, including programming languages, but this varies from application to application.

List of programming languages understood by ChatGPT

Language models serve as the foundation for generative artificial intelligence applications. That is, they can create responses according to the desires given through inputs. For example, ChatGPT produces text that, at its best, cannot be distinguished from text written by a human. In addition, image generators like DALL-E 3 can create and modify images based on the text inputs given to them. Each application can be taught its own specific task through machine learning. Machine learning is a subfield of artificial intelligence aimed at enabling an application to perform better based on foundational knowledge and possible user actions. The apparent intelligence results from the vast resource to calculate and compare within its language model to the generalization about subjects it has formed from a massive amount of text material. It cannot create anything new at random, but it combines existing data in entirely new ways and at a speed that humans are not capable of.

Limitations of generative artificial intelligence

Artificial intelligence reflects the source material fed to it. As a limitation to use, you have to remember the possible incorrectness of the information, i.e. hallucinations in the answer. In addition, it may offer biased or damaging information due to the source material used in teaching the language model. This is because the vast majority of the source material used to teach the language model comes from Western countries. The fact that materials from, for example, China or Africa have not been used as source material, weakens the quality of the application and the ability to produce impartial and equal information, taking into account different cultures. The responsibility for the correctness of the written information rests with the artificial intelligence user. Artificial intelligence itself does not care if something is true that it generates for the user, because its task is only to generate text, for example.

GPT-3

GPT-3 (Generative Pre-training Transformer 3) is the third version of the language model developed by OpenAI, released in spring 2020. It is trained with a large amount of text to predict the next word in a sequence of words based on what words are before it (“if-then”- rule parameters) . For example, if the model is given the words “The man listens”, it will predict the next word to be “music”. There are 175 billion of these parameters in this language model.

The limitation is the incorrectness of the information, i.e. hallucination. In addition, it may offer biased or damaging information due to the source material used in the teaching of the language model. In addition, the data in the dataset has not been updated after December 2021.

Version 3.5 of this language model was the engine of the application ChatGPT, which was released in late November 2022.

GPT-4

This latest version of the language model was published on March 14, 2023. The number of parameters used by the language model or the size of the data model have not been disclosed, but it is said to be more creative, understand more complex instructions and be able to solve more complex problems than previous language models. Public data (internet) and licensed third-party libraries have been used as source material for the training of the language model.

It masters and helps with more demanding and complex creative and technical writing tasks, such as composing songs, writing scripts or learning a user’s writing style. In addition, GPT-4 accepts images as input and can create descriptive texts from the content of images, classifications and analyses. The ability to handle larger amounts of text as input has also been improved. GPT-4 can handle more than 25,000 words of text, enabling use cases such as long-form content creation, extended discussions, and document search and analysis.

In addition to efficiency, GPT-4 is more accurate in terms of data accuracy. Accuracy has been increased to 70-80%, depending on the subject. GPT-3.5 got on average 50-60% of the facts correct. But can still provide incorrect or biased information, like its predecessor. Its dataset covers the period until December 2022.

This new language model is currently used in the paid version of ChatGPT, ChatGPT Plus.

GPT-4o (omni), ChatGPT language model (opens in a new browser window)

GPT-4o (“o” stands for “omni”) was released on May 13, 2024, and represents a significant advancement in natural human-computer interaction. The model offers real-time reasoning capabilities through text, audio, and image inputs, enabling natural and versatile means of interaction with the application. GPT-4o communicates with users via text, audio, and image inputs (prompts), creating dialogues and providing answers to new questions, and, if necessary, asking the user for clarifications.

Incorporating fast response times for audio inputs, GPT-4o strives to mimic human conversation, providing a seamless user experience for dialogue. The model demonstrates enhanced performance in image and audio understanding compared to previous versions. By combining text, audio, and image processing into a single input, GPT-4o streamlines its input-output process, thereby improving its efficiency. It matches the performance of GPT-4 Turbo for English text and code, but offers significant improvements in handling other languages, while being significantly faster and 50% more affordable when accessed through the API than GPT-4 Turbo.

OpenAI has conducted extensive evaluations of GPT-4o’s capabilities, including text, audio, and image understanding, and has demonstrated its performance. GPT-4o is now available for free to all users, with premium options offering higher capacity limits. Developers can use GPT-4o via the API for text and image processing, and there are plans to add audio and video capabilities in the future.

GPT-o1, reasoning ability (opens in a new browser window)

On September 12, 2024, OpenAI has introduced a large language model called o1, which is trained to perform complex reasoning using reinforcement learning. o1 is designed to think step by step before responding, using a “chain of thought” process.

Key features of OpenAI o1 preview:

  • Improved reasoning: o1 shows better reasoning abilities compared to its predecessor, GPT-4o, in various benchmarks and tests.
  • Chain of Thought Reasoning: The model uses a chain of thought process, mimicking human reasoning by breaking down problems into smaller steps, identifying errors, and exploring alternative approaches.
  • Human-level performance: o1 achieves impressive results in standardized tests and benchmarks, even surpassing the performance of human experts in certain areas, such as GPQA-diamond assessments, which measure scientific expertise.
  • Improved coding abilities: o1 demonstrates strong coding abilities, ranking in the 89th percentile on Codeforces programming challenges and outperforming GPT-4o on coding tasks.

Benefits of chain of thought reasoning:

  • Transparency: The chain of thought provides a clear representation of the model’s reasoning process, allowing developers to view and understand its decision-making.
  • Security and Congruence: Integrating security practices into the chain of thought has shown promising results in improving model security and congruence with people’s values.

Limitations and considerations:

  • Not ideal for all tasks: While o1-preview performs well in reasoning-intensive domains, it may not be suitable for all natural language processing tasks, as found in human preference tests.
  • Visibility of the chain of thoughts: Currently, the raw chain of thoughts is not shown directly to users, but instead a summary produced by the model is used. This decision seeks to strike a balance between transparency, user experience, and potential abuse.

Overall, the OpenAI o1 preview represents a significant step forward in AI inference, expanding the boundaries of the model’s capabilities and opening up new possibilities for AI applications in various fields.

Links (open in a new browser window)

Here’s how to make a screen capture video

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With screen capture video, you can record the computer screen, sound and video image. There are different applications for screen capture that have certain characteristics. By familiarizing yourself with the listing on the page and the tool-specific instructions, and by experimenting, you will surely find the right tool for you.

You should also familiarize yourself with the screen capture memo list .

Take advantage of the screen capture, for example, when

  • You want to make a work instruction: “do these steps with a computer program so that you get this as a result”
  • You save a PowerPoint presentation in which you explain in more detail about the content of the different slides.
  • Annotate: you use drawing tools or comment on the video, photos or other content playing on the screen.

The frame recording video is saved locally on your own computer. You can use the recording as part of another video or publish the video in Kaltura.

Tools for making screen capture videos

ScreenCast-O-Matic

  • With SOM, you can make a traditional screen capture, where the screen, web camera input and sound are recorded according to your choice. You can also add subtitles to the video semi-automatically and edit it if necessary.
  • If you want to make a capture in smaller parts, when the so-called you don’t have to get everything ready at once, you can use the Scripting tool to prepare your video step by step. At the same time, subtitles and a spoken part are created for the video.
  • Read more about SOM: Screencast-O-Matic for screen recording, editing and subtitling to produce.

PPT recording

  • With PowerPoint’s own recording tool, you can make a video of your presentation, to which you can also attach a narration and a webcam image.
  • The videos are recorded per slide, so you can take the narration of the slide that was lost and continue forward in the presentation. This way you don’t have to cut the video or start over every time.
  • You can also take a screenshot with PowerPoint’s capture tool and insert it into a PowerPoint presentation.
  • Read more about PPT recordings: screen recording with PowerPoint.

Kaltura

  • With Kaltura’s Personal Capture tool, you can make a traditional screen capture, where the screen, web camera input and sound are recorded according to your choice.
  • If you make a recording of a PowerPoint presentation, the content of the slides will be recognized automatically and a subtitle will be created for each slide using the Timeline function, help: Kaltura timeline.
  • Read more about Kaltura Personal Capture application.

OBS

  • OBS is a more advanced tool that enables many different functions related to capturing and recording image and sound.
  • With OBS, you can make countless fine adjustments to your recording. You can import an image from a screen, web camera or video capture card. You can build different scans with different settings and use them as needed, as well as send and receive RTMP/RTSP streams.
  • Due to the abundance of functionalities, the use may seem challenging at first, but after learning for a while, OBS is a suitable solution for many situations.
  • Read more about OBS.

Teams and Zoom

You can also record the screen in Teams and Zoom sessions. Instructions for recording a Teams session and instructions for recording a Zoom session.

If you wish, you can use HAMK’s ready-made base as opening images or title slides, document download link.

Comparison of the functions of Teams and Zoom

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In this listing, the functions of Teams and Zoom have been collected side by side, as well as a comparison of their features and tools. The purpose of the guide is to make it easier to choose a suitable platform for the given online event. This guide was last updated in October 2022, Please note that the features are constantly changing.

You can follow the latest features of the platforms on the services’ own websites:

Table: comparison of functions

FunctionTeamsZoom
Restricting access to a session· Waiting room.
· Registration (webinar and live event).		· A waiting room in a regular session.
· In a webinar practice session.
· Password.
· Identification.
· Registration.
· Restriction on joining the session only when the host is present.
· Registration and identification in the webinar.
Host rights· By default, one host with all rights.
· you can add co-organizers from the meeting settings.
· Read more: Add co-organizers to a meeting in Teams (Microsoft)		· can add alternative hosts, help during the session from co-hosts.
· In the webinar, the host and co-host also play the role of panelist.
Max participants· 1000 active participants, after which participants can join the meeting with “view-only” rights, i.e. they do not use the meeting’s functionalities. Read more: view-only experience (Microsoft).·1,000 people, additionally expandable to e.g. a YouTube stream.
· larger audiences can also be arranged.
Interaction· Polls in a Teams meeting.
· Chat.
· Short-term reactions and permanent reactions to messages.
· Raise hands.
· Whiteboard Teams meeting (Microsoft)		· polls (Zoom instructions )
· chat (Zoom instructions)
· annotation (link to video on Youtube)
· short-term reactions such as thumbing.
(Zoom instructions)
· yes-no buttons.
· show of hands.
· Whiteboards (link to video on Youtube).
https://digipedaohjeet.hamk. en/help/adding-interaction-to-zoom-session/
Recording· To OneDrive, recording cannot be paused (pause).· locally to your own device, recording can be paused (pause).
Sharing the recording· Share the video file with a link or email address.
· Share via the video service.
· Read more: Recording and sharing a Teams meeting (Digipeda instructions).		· sharing via video service.
Chat· remains available to participants after the meeting, turned off completely or only during the session (the host controls the choice).· the participant can only see the messages sent after joining the session.
· also moderated Q&A possible (webinar).
· Conversations should be saved separately or according to your automatic saving setting.
Microphones· in the meeting, the host can mute and specify that it cannot be opened anymore.· in a normal session, the host can mute and specify that it cannot be opened anymore or disabled.
· not for use by participants in the webinar.
(Zoom instructions)
Web cameras· host can manage or completely disable.· the host can manage or completely disable it (not at all used by the participants in the webinar).
(Zoom instructions)
Small group work· The host guides small group work and, for example, can move participants from one room to another.
Read more: Teams small group spaces (Digipeda instructions).		· host and co-hosts guide small group work.
· participants can also move freely from room to room.
Session functions· According to updates distributed by Microsoft.· the host can set in advance in detail the on/off functions that are used in the session/webinar.
· during the session/webinar the host can comprehensively manage all the functions that the participants use.
File sharing· In chat as files, otherwise as web links.· in chat as files, otherwise as www links.
Subtitling in the session· Speech recognition, this is a participant-specific view.
· Transcript function available in the session, which writes the speeches into text. The file can be downloaded to the computer after the meeting.		· automatically works in English (link), automatically works in Finnish through third-party applications (link), manually (link).
Virtual wallpapers· Yes.· Yes ( Zoom instructions)
Session duration max· 24 hours.· 30 hours (logged in HAMK user)
Participant lists, etc. reports· host can save an Excel file.· the host can view the sessions held within the month, parents with a Helpdesk ticket.
Participation by browser· Edge or Chrome recommended.· recommended Google Chrome, Mozilla Firefox and Chromium Edge (Zoom Instructions)
Mobile appYes.Yes. (Instructions: Android ja iOS)
Licenses· Staff have an extensive A5 license, students A5 Student Benefit. Read more about licenses on the Microsoft Education page (in English).· full license, for 500 participants · webinar license available for loan with Helpesk ticket.

Microsoft’s analytics tools help with student guidance

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Learning analytics helps in teaching planning, student guidance and implementation development. There is a lot of information, the most important thing is to identify the most relevant information that supports the pedagogical model chosen for implementation.

Sway

Sway’s own analytics show the following information for a shared individual presentation:

  • views
  • time spent on average
  • what percentage have read through
  • how many have viewed the presentation
  • how many people have read the presentation
  • how many have deeply read through the presentation.

For example, you can use Sway’s analytics to get an idea of how many people have read the pre-shared material (flipped classroom).

The analytics information can be seen when you are logged in on the Sway home screen under the presentation.

Microsoft Stream

Currently, the service offers limited statistics related to videos. You can see how many people have watched, liked or commented on the video.

This instruction was produced in the APOA project. The APOA project is financed by the Ministry of Education and Culture. The project pilots and investigates the use of learning analytics in universities of applied sciences. More information about the project http://apoa.tamk.fi/