With one of our latest updates, we also gave Nina the ability to recognize returning visitors (completely anonymously, of course). This allows the conversation to resume. This gives the learners a personal feel and also reduces the number of interactions needed to get to the learning goal.

One of the most crucial factors that led to Nina’s successful deployment is her ability to analyze user sentiment. The data that Nina collects from all students is turned into insightful ‘learning analytics’ by Helpr.

Thieme Meulenhoff can then use this data for various purposes. For instance, we can recognize a trend from the ‘learning analytics’ that a lot of students experience exam stress in February instead of March. With that insight, Thieme Meulenhoff could reassure students in time by offering them support and preparatory exam materials, among other things.

By gaining insight in such trends, Thieme Meulenhoff can respond to user feedback in a targeted way. With the likely outcome that the useful use of real-time feedback contributes to a better customer experience.

VVCR-Prodrive creates microlearnings based on ‘conversational learning’. This involves an interactive learning conversation that makes the learning experience more dynamic. In addition to using conversational learning, VVCR-Prodrive also uses ‘blended learning’.

This allows one to follow a digital theory training with an interactive conversation which is complemented by physical training on the road. At VVCR-Prodrive, the roadsafety experts transfer their knowledge and experience in a personal manner during the practical training sessions. This is then supplemented by using the e-learning courses on the digital learning platform. These build on the lessons already learned.

The choice for this combination of learning was an obvious one: VVCR-Prodrive sees education and development in the field of traffic safety as a continuous process. Short ‘nudges’ are proven to be effective and an online environment helps to convey them!

We help VVCR-Prodrive make their online content even better. We make the e-learnings more interactive, personalized, and educational. Besides delivering great learning journeys, our learning bots can also remember previous conversations with end-users.

These can then be used as input for later moments to revisit important topics. If our learning bot is told during the first training session that the user drives quite sportily, the learning bot will remember this for all subsequent training sessions. In this way the learning bot unerringly adapts the content to the user. The bot collects information about the type of user it has in front of it and thus makes every training session personal. The learning bot also stores all the answers (completely anonymously, of course).

The data is then converted into Learning Analytics that VVCR-Prodrive and its clients can use for various purposes. Using those statistics, they can iteratively improve their content.

Een van de voornaamste redenen waarom Dean zo goed blijkt te werken, is dat hij ongemotiveerde thuiszitters met een mooie belofte geactiveerd weet te krijgen. Dean belooft hen namelijk dat hij ze binnen 10 minuten van ‘geen flauw idee hebben welk beroep ze willen volgen’, naar een interessant beroepskeuze kan begeleiden. Ze hoeven hiervoor niet naar een fysieke locatie om een lang gesprek met iemand te voeren. thuiszitters kunnen geheel vrijblijvend vanaf de bank actie ondernemen. Interessant hierbij is dat Dean vooropleidingen en beroepen voorselecteert op basis van toekomstbestendigheid. Dit doet hij aan de hand van betrouwbare input over de huidige en toekomstige robotisering en automatiseringsgraad van beroepen.

Vooralsnog werkt Dean het beste in de metropoolregio Amsterdam. Dit komt omdat hij Amsterdam centraal station als het centrale punt neemt waar vanaf hij de bereikbaarheid van de opleidingslocaties berekent. Echter kan Dean ook makkelijk worden ingezet om thuiszitters in andere regio’s effectief te kunnen helpen. Het centrale punt wordt dan simpelweg bijgesteld. Het is ons doel om hetgeen waar studiebegeleiders en de leerplicht soms tekort schiet, te ondersteunen met Dean. Zo dragen wij ons steentje bij aan een generatie aan leerlingen die later met plezier naar hun werk toe gaat!

Nina has been employed as a study coach since the early 2020s, guiding students with their learning. Communication with students used to be mainly offline. Now students can use digital learning conversations to indicate how they are feeling and what they need help with.

Among other things, Nina asks how they are doing, if they are nervous about an exam, or, for example, in what way they have already learned for the exams. Furthermore, Nina helps students learn proven learning strategies and also functions as a search function within the website.

Nina is relevant every month thanks to the ‘content calendar’. This allows Nina to know when certain exam products are most relevant to the students. The school level is linked to the months of the year. Nina can therefore not