The Effect of Aleks on Students\u27 Mathematics Achievement in an Online Learning Environment and the Cognitive Complexity of the Initial and Final Assessments

For many courses, mathematics included, there is an associated interactive e-learning system that provides assessment and tutoring. Some of these systems are classified as Intelligent Tutoring Systems. MyMathLab, Mathzone, and Assessment of LEarning in Knowledge Space (ALEKS) are just a few of the interactive e-learning systems in mathematics. In ALEKS, assessment and tutoring are based on the Knowledge Space Theory. Previous studies in a traditional learning environment have shown ALEKS users to perform equally or better in mathematics achievement than the group who did not use ALEKS. The purpose of this research was to investigate the effect of ALEKS on students’ achievement in mathematics in an online learning environment and to determine the cognitive complexity of mathematical tasks enacted by ALEKS’s initial (pretest) and final (posttest) assessments. The targeted population for this study was undergraduate students in College Mathematics I, in an online course at a private university in the southwestern United States. The study used a quasi-experimental One-Group non-randomized pretest and posttest design. Five methods of analysis and one model were used in analyzing data: t-test, correctional analysis, simple and multiple regression analysis, Cronbach’s Alpha reliability test and Webb’s depth of knowledge model. A t-test showed a difference between the pretest and posttest reports, meaning ALEKS had a significant effect on students’ mathematics achievement. The correlation analysis showed a significant positive linear relationship between the concept mastery reports and the formative and summative assessments reports meaning there is a direct relationship between the ALEKS concept mastery and the assessments. The regression equation showed a better model for predicting mathematics achievement with ALEKS when the time spent learning in ALEKS and the concept mastery scores are used as part of the model. According to Webb’s depth of knowledge model, the cognitive complexity of the pretest and posttest question items used by ALEKS were as follows: 50.5% required application of skills and concepts, 37.1% required recall of information, and 12.4% required strategic thinking: None of the questions items required extended thinking or complex reasoning, implying ALEKS is appropriate for skills and concepts building at this level of mathematics

Revita: a Language-learning Platform at the Intersection of ITS and CALL

This paper presents Revita, a Web-based platform for language learning—beyond the beginner level. We anchor the presentation in a survey, where we review the literature about recent advances in the fields of computer-aided language learning (CALL) and intelligent tutoring systems (ITS). We outline the established desiderata of CALL and ITS and discuss how Revita addresses (the majority of) the theoretical requirements of CALL and ITS. Finally, we claim that, to the best of our knowledge, Revita is currently the only platform for learning/tutoring beyond the beginner level, that is functional, freely-available and supports multiple languages.Peer reviewe

ALEAS: a tutoring system for teaching and assessing statistical knowledge

Over the years, several studies have shown the relevance of one-to-one compared to one-to-many tutoring, shedding light on the need for technology-based platforms to assist traditional learning methodologies. Therefore, in recent years, tutoring systems that collect and analyse responses during the user interaction for an automated assessment and profiling were developed as a new standard to improve the learning out- come. In this framework, the tutoring system Adaptive LEArning system for Statistics (ALEAS) is aimed at providing an adaptive assessment of undergraduate students’ statistical abilities enrolled in social and human sciences courses. ALEAS is developed in the contest of the ERAS- MUS+ Project (KA+ 2018-1-IT02-KA203-048519). The article describes the ALEAS workflow; in particular, it focuses on the students’ categorisation according to their abilities. The student follows a learning process defined according to the Knowledge Space Theory, and she/he is classified at the end of each learning unit. The proposed classification method is based on the multidimensional latent class item response theory, where the dimensions are defined according to the Dublin learning dimensions. In this work, results from a simulation study support our approach’s effectiveness and encourage its future use with students

Knowledge Spaces and Learning Spaces

How to design automated procedures which (i) accurately assess the knowledge of a student, and (ii) efficiently provide advices for further study? To produce well-founded answers, Knowledge Space Theory relies on a combinatorial viewpoint on the assessment of knowledge, and thus departs from common, numerical evaluation. Its assessment procedures fundamentally differ from other current ones (such as those of S.A.T. and A.C.T.). They are adaptative (taking into account the possible correctness of previous answers from the student) and they produce an outcome which is far more informative than a crude numerical mark. This chapter recapitulates the main concepts underlying Knowledge Space Theory and its special case, Learning Space Theory. We begin by describing the combinatorial core of the theory, in the form of two basic axioms and the main ensuing results (most of which we give without proofs). In practical applications, learning spaces are huge combinatorial structures which may be difficult to manage. We outline methods providing efficient and comprehensive summaries of such large structures. We then describe the probabilistic part of the theory, especially the Markovian type processes which are instrumental in uncovering the knowledge states of individuals. In the guise of the ALEKS system, which includes a teaching component, these methods have been used by millions of students in schools and colleges, and by home schooled students. We summarize some of the results of these applications

An Exploratory Comparison of a Traditional and an Adaptive Instructional Approach for College Algebra

This research effort compared student learning gains and attitudinal changes through the implementation of two varying instructional approaches on the topic of functions in College Algebra. Attitudinal changes were measured based on the Attitude Towards Mathematics Inventory (ATMI). The ATMI also provided four sub-scales scores for self-confidence, value of learning, enjoyment, and motivation. Furthermore, this research explored and compared relationships between students\u27 level of mastery and their actual level of learning. This study implemented a quasi-experimental research design using a sample that consisted of 56 College Algebra students in a public, state college in Florida. The sample was enrolled in one of two College Algebra sections, in which one section followed a self-adaptive instructional approach using ALEKS (Assessment and Learning in Knowledge Space) and the other section followed a traditional approach using MyMathLab. Learning gains in each class were measured as the difference between the pre-test and post-test scores on the topic of functions in College Algebra. Attitude changes in each class were measured as the difference between the holistic scores on the ATMI, as well as each of the four sub-scale scores, which was administered once in the beginning of the semester and again after the unit of functions, approximately eight weeks into the course. Utilizing an independent t-test, results indicated that there was not a significant difference in actual learning gains for the compared instructional approaches. Additionally, independent t-test results indicated that there was not a statistical difference for attitude change holistically and on each of the four sub-scales for the compared instructional approaches. However, correlational analyses revealed a strong relationship between students\u27 level of mastery learning and their actual learning level for each class with the self-adaptive instructional approach having a stronger correlation than the non-adaptive section, as measured by an r-to-z Fisher transformation test. The results of this study indicate that the self-adaptive instructional approach using ALEKS could more accurately report students\u27 true level of learning compared to a non-adaptive instructional approach. Overall, this study found the compared instructional approaches to be equivalent in terms of learning and effect on students\u27 attitude. While not statistically different, the results of this study have implications for math educators, instructional designers, and software developers. For example, a non-adaptive instructional approach can be equivalent to a self-adaptive instructional approach in terms of learning with appropriate planning and design. Future recommendations include further case studies of self-adaptive technology in developmental and college mathematics in other modalities such as hybrid or on-line courses. Also, this study should be replicated on a larger scale with other self-adaptive math software in addition to focusing on other student populations, such as K - 12. There is much potential for intelligent tutoring to supplement different instructional approaches, but should not be viewed as a replacement for teacher-to-student interactions

Modeling knowledge states in language learning

Artificial intelligence (AI) is being increasingly applied in the field of intelligent tutoring systems (ITS). Knowledge space theory (KST) aims to model the main features of the process of learning new skills. Two basic components of ITS are the domain model and the student model. The student model provides an estimation of the state of the student’s knowledge or proficiency, based on the student’s performance on exercises. The domain model provides a model of relations between the concepts/skills in the domain. To learn the student model from data, some ITSs use the Bayesian Knowledge Tracing (BKT) algorithm, which is based on hidden Markov models (HMM). This thesis investigates the applicability of KST to constructing these models. The contribution of the thesis is twofold. Firstly, we learn the student model by a modified BKT algorithm, which models forgetting of skills (which the standard BKT model does not do). We build one BKT model for each concept. However, rather than treating a single question as a step in the HMM, we treat an entire practice session as one step, on which the student receives a score between 0 and 1, which we assume to be normally distributed. Secondly, we propose algorithms to learn the “surmise” graph—the prerequisite relation between concepts—from “mastery data,” estimated by the student model. The mastery data tells us the knowledge state of a student on a given concept. The learned graph is a representation of the knowledge domain. We use the student model to track the advancement of students, and use the domain model to propose the optimal study plan for students based on their current proficiency and targets of study

Exploring the Link Between Students’ Usage of ALEKS and Their Performance on State Benchmark Assessments in Mathematics

The purpose of this mixed methods study was to investigate relationships between students’ ALEKS usage, teachers’ implementation of ALEKS, and student performance on the 2017 – 2018 LEAP 2025 mathematics assessment. The quantitative portion of the study involved district-level analyses and teacher-level analyses that explored relationships between students’ ALEKS usage and LEAP performance. The qualitative portion of the study took into consideration previous research findings that have reported associations between program implementation and student achievement. This portion of the study included thematic analyses that examined the following relationships: ALEKS implementation in relation to teacher groups (i.e., RtI 8, Math 8, Both, and Magnet), ALEKS implementation of each teacher and LEAP performance, and ALEKS implementation in relation to teacher rankings (i.e., high student achievement or HSA / low student achievement or LSA) and LEAP performance. Key findings from the quantitative analyses indicated that ALEKS usage in terms of time spent and concept mastery did not make a statistically significant impact on students’ LEAP performance for any of the teachers except one teacher. In contrast, ALEKS usage in terms of skill mastery made a statistically significant impact on students’ LEAP performance for HSA teachers and for one LSA teacher. However, low usage of ALEKS in terms of time spent limited my ability to fully assess the potential impact of ALEKS usage on students’ LEAP performance. Key findings from the qualitative analyses indicated that there were differences in ALEKS implementation amongst teacher groups. To control for group differences, this study focused on Math 8 teachers who used the ALEKS Middle School Math Course 3 curriculum; these teachers were ranked into student achievement groups HSA and LSA. In essence, ALEKS implementation of HSA teachers were more closely aligned with ALEKS (2017) Best Practices for program implementation compared to LSA teachers. ALEKS implementation of LSA teachers typically deviated from ALEKS (2017) Best Practices. Overall, these findings suggest that despite low usage of ALEKS in terms of time spent, teachers who more closely followed the recommendations of ALEKS (2017) Best Practices had positive statistically significant associations between students’ skill mastery on ALEKS and LEAP performance

ALEKS Constructs as Predictors of High School Mathematics Achievement for Struggling Students

Educators in the United States (U.S.) are increasingly turning to intelligent tutoring systems (ITS) to provide differentiated math instruction to high school students. However, many struggling high school learners do not perform well on these platforms, which reinforces the need for more awareness about effective supports that influence the achievement of learners in these milieus. The purpose of this study was to determine what factors of the Assessment and Learning in Knowledge Spaces (ALEKS), an ITS, are predictive of struggling learners\u27 performance in a blended-learning Algebra 1 course at an inner city technical high school located in the northeastern U.S. The theoretical framework consisted of knowledge base theory, the zone of proximal development, and cognitive learning theory. Three variables (student retention, engagement time, and the ratio of topics mastered to topics practiced) were used to predict the degree of association on the criterion variable (mathematics competencies), as measured by final course progress grades in algebra, and the Preliminary Scholastic Assessment Test (PSATm) math scores. A correlational predictive design was applied to assess the data of a purposive sample of 265 struggling students at the study site; multiple regression analysis was also used to investigate the predictability of these variables. Findings suggest that engagement time and the ratio of mastered to practiced topics were significant predictors of final course progress grades. Nevertheless, these factors were not significant contributors in predicting PSATm score. Retention was identified as the only statistically significant predictor of PSATm score. The results offer educators with additional insights that can facilitate improvements in mathematical content knowledge and promote higher graduation rates for struggling learners in high school mathematics

Multi-scenario modelling of learning

International audienceDesigning an educational scenario is a sensitive and challenging activity because it is the vector of learning. However, the designed scenario may not correspond to some learners’ characteristics (pace of work, cognitive styles, emotional factors, prerequisite knowledge, …). To personalize the learning task and adapt it gradually to each learner, several scenarios are needed. Adaptation and personalization are difficult because it is necessary on the one hand to know in advance the profiles and on the other hand to produce the multiple scenarios corresponding to these profiles. Our model allows to design many scenarios without knowing the learner profiles beforehand. Furthermore, it offers each learner opportunities to choose a scenario and to change it during their learning process. The model ensures that all announced objectives have enough resources for acquiring knowledge and activities for evaluation