Whenever students use any drilling system the question arises how much of their learning is meaningful learning, which emphasises understanding and the transferability of prior knowledge, and how much is memorisation through repetition or rote learning. Although both types of learning have their place in an educational system it is important to be able to distinguish between these two approaches to learning and identify options which can dislodge students from rote learning and motivate them towards meaningful learning.The tutor-web is an online drilling system, which has been used by thousands of students from Iceland to Kenya. The design aim of the system is to promote meaningful learning rather than evaluation. This is done by presenting students with multiple-choice questions which are selected randomly but nevertheless linked to the students' performance to ensure that students are appropriately challenged. The questions themselves can be generated for a specific topic by drawing correct and incorrect answers from a collection associated with a general problem statement or heading. With this generating process students may see the same question heading twice but be presented with all new answer options or a mixture of new and old answer options.Data from an introductory university course on probability theory and statistics, taught using the tutorweb during COVID-19, are analysed to separate rote learning from meaningful learning. The analyses show that considerable non-rote learning takes place, but even with fairly large question databases, students' performance is considerably better when they are presented with an answer option they have seen before. An element of rote learning is thus clearly exhibited but a deeper learning is also demonstrated.The item database has been seeded with occasional hints such that some questions contain fairly detailed clues, which should cue the students towards the correct answer. This ties in with the issue of meaningful learning versus rote learning since the hope is that a new hint will work as a cue to coax the student to think harder about the question rather than continue to employ rote learning. The existence of occasional hints allows several comparisons. The simplest analysis is on whether the overall grade on cue questions is higher than on the non-cue questions. A more important issue is whether more learning has occurred and methods are developed to estimate the change rather than status. Preliminary results indicate that hints are particularly useful for students with poor performance metrics, and a power analysis demonstrates the sample sizes needed in future studies to better quantify these effects.

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a b s t r a c tResearch is described on a system for web-assisted education and how it is used to deliver on-line drill questions, automatically suited to individual students. The system can store and display all of the various pieces of information used in a class-room (slides, examples, handouts, drill items) and give individualized drills to participating students. The system is built on the basic theme that it is for learning rather than evaluation. Experimental results shown here imply that both the item database and the item allocation methods are important and examples are given on how these need to be tuned for each course. Different item allocation methods are discussed and a method is proposed for comparing several such schemes. It is shown that students improve their knowledge while using the system. Classical statistical models which do not include learning, but are designed for mere evaluation, are therefore not applicable. A corollary of the openness and emphasis on learning is that the student is permitted to continue requesting drill items until the system reports a grade which is satisfactory to the student. An obvious resulting challenge is how such a grade should be computed so as to reflect actual knowledge at the time of computation, entice the student to continue and simultaneously be a clear indication for the student. To name a few methods, a grade can in principle be computed based on all available answers on a topic, on the last few answers or on answers up to a given number of attempts, but all of these have obvious problems.

Enhancing the mathematics classroom using computerised drills for homework is shown to provide considerable benefits for student learning in diverse regions, from high-tech westerns classrooms to those with neither Internet nor stable electricity. This paper describes results from several experiments using the tutor-web educational system for teaching mathematics with student groups ranging from upper primary school through graduate school. When existing teaching methods are augmented using this technology, quantitative and qualitative results across all regions and age groups indicate improvements in learner performance. Although it has a wide range of functions, the tutor-web system is most importantly a drilling system, which would normally be classified as an Adaptive and Intelligent Web-based Educational System (AIWBES) providing quite personalised learning, tailoring a drill sequence for each student. In addition to personalised grading schemes aimed for enticing the student to continue, a different reward scheme is implemented by giving the students a cryptocurrency whenever they complete a topic with excellence. Initial results of the effects of these various reward schemes are also presented

The tutor‐web is an open‐source learning environment designed to be used for teaching mathematics and statistics. The system offers thousands of exercises at high school and university level, and has been used for a decade to teach introductory statistics courses with good results. A new component has recently been added to the tutor‐web so that students can enter their own data or get real data from several data sources for practicing and learning new concepts.